des

mmdetection

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-Subproject commit bce85dc84dcae85bab87c171481acff7b3df25ad

mmdetection/.circleci/config.yml

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+version: 2.1
+
+# this allows you to use CircleCI's dynamic configuration feature
+setup: true
+
+# the path-filtering orb is required to continue a pipeline based on
+# the path of an updated fileset
+orbs:
+  path-filtering: circleci/path-filtering@0.1.2
+
+workflows:
+  # the always-run workflow is always triggered, regardless of the pipeline parameters.
+  always-run:
+    jobs:
+      # the path-filtering/filter job determines which pipeline
+      # parameters to update.
+      - path-filtering/filter:
+          name: check-updated-files
+          # 3-column, whitespace-delimited mapping. One mapping per
+          # line:
+          # <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
+          mapping: |
+            mmdet/.* lint_only false
+            requirements/.* lint_only false
+            tests/.* lint_only false
+            tools/.* lint_only false
+            configs/.* lint_only false
+            .circleci/.* lint_only false
+          base-revision: dev-3.x
+          # this is the path of the configuration we should trigger once
+          # path filtering and pipeline parameter value updates are
+          # complete. In this case, we are using the parent dynamic
+          # configuration itself.
+          config-path: .circleci/test.yml

mmdetection/.circleci/docker/Dockerfile

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+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx

mmdetection/.circleci/test.yml

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+version: 2.1
+
+# the default pipeline parameters, which will be updated according to
+# the results of the path-filtering orb
+parameters:
+  lint_only:
+    type: boolean
+    default: true
+
+jobs:
+  lint:
+    docker:
+      - image: cimg/python:3.7.4
+    steps:
+      - checkout
+      - run:
+          name: Install pre-commit hook
+          command: |
+            pip install pre-commit
+            pre-commit install
+      - run:
+          name: Linting
+          command: pre-commit run --all-files
+      - run:
+          name: Check docstring coverage
+          command: |
+            pip install interrogate
+            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 85 mmdet
+
+  build_cpu:
+    parameters:
+      # The python version must match available image tags in
+      # https://circleci.com/developer/images/image/cimg/python
+      python:
+        type: string
+      torch:
+        type: string
+      torchvision:
+        type: string
+    docker:
+      - image: cimg/python:<< parameters.python >>
+    resource_class: large
+    steps:
+      - checkout
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5
+      - run:
+          name: Configure Python & pip
+          command: |
+            pip install --upgrade pip
+            pip install wheel
+      - run:
+          name: Install PyTorch
+          command: |
+            python -V
+            python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - when:
+          condition:
+            equal: ["3.9.0", << parameters.python >>]
+          steps:
+            - run: pip install "protobuf <= 3.20.1" && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake
+            - run: pip install dsdl
+      - run:
+          name: Install mmdet dependencies
+          # numpy may be downgraded after building pycocotools, which causes `ImportError: numpy.core.multiarray failed to import`
+          # force reinstall pycocotools to ensure pycocotools being built under the currenct numpy
+          command: |
+            python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main
+            pip install -U openmim
+            mim install 'mmcv >= 2.0.0rc4'
+            pip install -r requirements/tests.txt -r requirements/optional.txt
+            pip install --force-reinstall pycocotools
+            pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+            pip install -r requirements/tracking.txt
+            pip install git+https://github.com/cocodataset/panopticapi.git
+            pip install git+https://github.com/JonathonLuiten/TrackEval.git
+      - run:
+          name: Build and install
+          command: |
+            pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            python -m coverage run --branch --source mmdet -m pytest tests/
+            python -m coverage xml
+            python -m coverage report -m
+
+  build_cuda:
+    parameters:
+      torch:
+        type: string
+      cuda:
+        type: enum
+        enum: ["11.1", "11.7"]
+      cudnn:
+        type: integer
+        default: 8
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+      # docker_layer_caching: true
+    resource_class: gpu.nvidia.small
+    steps:
+      - checkout
+      - run:
+          # CLoning repos in VM since Docker doesn't have access to the private key
+          name: Clone Repos
+          command: |
+            git clone -b main --depth 1 ssh://git@github.com/open-mmlab/mmengine.git /home/circleci/mmengine
+      - run:
+          name: Build Docker image
+          command: |
+            docker build .circleci/docker -t mmdetection:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
+            docker run --gpus all -t -d -v /home/circleci/project:/mmdetection -v /home/circleci/mmengine:/mmengine -w /mmdetection --name mmdetection mmdetection:gpu
+            docker exec mmdetection apt-get install -y git
+      - run:
+          name: Install mmdet dependencies
+          command: |
+            docker exec mmdetection pip install -e /mmengine
+            docker exec mmdetection pip install -U openmim
+            docker exec mmdetection mim install 'mmcv >= 2.0.0rc4'
+            docker exec mmdetection pip install -r requirements/tests.txt -r requirements/optional.txt
+            docker exec mmdetection pip install pycocotools
+            docker exec mmdetection pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+            docker exec mmdetection pip install -r requirements/tracking.txt
+            docker exec mmdetection pip install git+https://github.com/cocodataset/panopticapi.git
+            docker exec mmdetection pip install git+https://github.com/JonathonLuiten/TrackEval.git
+            docker exec mmdetection python -c 'import mmcv; print(mmcv.__version__)'
+      - run:
+          name: Build and install
+          command: |
+            docker exec mmdetection pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            docker exec mmdetection python -m pytest tests/
+
+workflows:
+  pr_stage_lint:
+    when: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - dev-3.x
+  pr_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - dev-3.x
+      - build_cpu:
+          name: minimum_version_cpu
+          torch: 1.8.0
+          torchvision: 0.9.0
+          python: 3.7.16
+          requires:
+            - lint
+      - build_cpu:
+          name: maximum_version_cpu
+          torch: 2.0.0
+          torchvision: 0.15.1
+          python: 3.9.0
+          requires:
+            - minimum_version_cpu
+      - hold:
+          type: approval
+          requires:
+            - maximum_version_cpu
+      - build_cuda:
+          name: mainstream_version_gpu
+          torch: 1.8.1
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "11.1"
+          requires:
+            - hold
+      - build_cuda:
+          name: maximum_version_gpu
+          torch: 2.0.0
+          cuda: "11.7"
+          cudnn: 8
+          requires:
+            - hold
+  merge_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - build_cuda:
+          name: minimum_version_gpu
+          torch: 1.8.0
+          cuda: "11.1"
+          filters:
+            branches:
+              only:
+                - dev-3.x

mmdetection/.dev_scripts/batch_test_list.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+
+# missing wider_face/timm_example/strong_baselines/simple_copy_paste/
+# selfsup_pretrain/seesaw_loss/pascal_voc/openimages/lvis/ld/lad/cityscapes/deepfashion
+
+# yapf: disable
+atss = dict(
+    config='configs/atss/atss_r50_fpn_1x_coco.py',
+    checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=39.4),
+)
+autoassign = dict(
+    config='configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py',
+    checkpoint='auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+carafe = dict(
+    config='configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.6),
+)
+cascade_rcnn = [
+    dict(
+        config='configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py',
+        checkpoint='cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth',
+        eval='bbox',
+        url='https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth', # noqa
+        metric=dict(bbox_mAP=40.3),
+    ),
+    dict(
+        config='configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py',
+        checkpoint='cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth',
+        url='https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth', # noqa
+        eval=['bbox', 'segm'],
+        metric=dict(bbox_mAP=41.2, segm_mAP=35.9),
+    ),
+]
+cascade_rpn = dict(
+    config='configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py', # noqa
+    checkpoint='crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+centernet = dict(
+    config='configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py',
+    checkpoint='centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=29.5),
+)
+centripetalnet = dict(
+    config='configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py',  # noqa
+    checkpoint='centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=44.7),
+)
+convnext = dict(
+    config='configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py', # noqa
+    checkpoint='cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=51.8, segm_mAP=44.8),
+)
+cornernet = dict(
+    config='configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py',
+    checkpoint='cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=41.2),
+)
+dcn = dict(
+    config='configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=41.3),
+)
+dcnv2 = dict(
+    config='configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.7),
+)
+ddod = dict(
+    config='configs/ddod/ddod_r50_fpn_1x_coco.py',
+    checkpoint='ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=41.7),
+)
+deformable_detr = dict(
+    config='configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py',
+    checkpoint='deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=44.5),
+)
+detectors = dict(
+    config='configs/detectors/detectors_htc-r50_1x_coco.py',
+    checkpoint='detectors_htc_r50_1x_coco-329b1453.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=49.1, segm_mAP=42.6),
+)
+detr = dict(
+    config='configs/detr/detr_r50_8xb2-150e_coco.py',
+    checkpoint='detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.1),
+)
+double_heads = dict(
+    config='configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.0),
+)
+dyhead = dict(
+    config='configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py',
+    checkpoint='atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=43.3),
+)
+dynamic_rcnn = dict(
+    config='configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='dynamic_rcnn_r50_fpn_1x-62a3f276.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.9),
+)
+efficientnet = dict(
+    config='configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py',
+    checkpoint='retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.5),
+)
+empirical_attention = dict(
+    config='configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py',  # noqa
+    checkpoint='faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.0),
+)
+faster_rcnn = dict(
+    config='configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.4),
+)
+fcos = dict(
+    config='configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py',  # noqa
+    checkpoint='fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.7),
+)
+foveabox = dict(
+    config='configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py',
+    checkpoint='fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.9),
+)
+fpg = dict(
+    config='configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py',
+    checkpoint='mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=43.0, segm_mAP=38.1),
+)
+free_anchor = dict(
+    config='configs/free_anchor/freeanchor_r50_fpn_1x_coco.py',
+    checkpoint='retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.7),
+)
+fsaf = dict(
+    config='configs/fsaf/fsaf_r50_fpn_1x_coco.py',
+    checkpoint='fsaf_r50_fpn_1x_coco-94ccc51f.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.4),
+)
+gcnet = dict(
+    config='configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py',  # noqa
+    checkpoint='mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.4, segm_mAP=36.2),
+)
+gfl = dict(
+    config='configs/gfl/gfl_r50_fpn_1x_coco.py',
+    checkpoint='gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.2),
+)
+ghm = dict(
+    config='configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py',
+    checkpoint='retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.0),
+)
+gn = dict(
+    config='configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py',
+    checkpoint='mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.1, segm_mAP=36.4),
+)
+gn_ws = dict(
+    config='configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=39.7),
+)
+grid_rcnn = dict(
+    config='configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py',
+    checkpoint='grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+groie = dict(
+    config='configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.3),
+)
+guided_anchoring = dict(
+        config='configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py',  # noqa
+        checkpoint='ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth',
+        url='https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth', # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=36.9),
+    )
+hrnet = dict(
+    config='configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py',
+    checkpoint='faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=36.9),
+)
+htc = dict(
+    config='configs/htc/htc_r50_fpn_1x_coco.py',
+    checkpoint='htc_r50_fpn_1x_coco_20200317-7332cf16.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=42.3, segm_mAP=37.4),
+)
+instaboost = dict(
+    config='configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py',
+    checkpoint='mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.6, segm_mAP=36.6),
+)
+libra_rcnn = dict(
+    config='configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.3),
+)
+mask2former = dict(
+    config='configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py',
+    checkpoint='mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth', # noqa
+    eval=['bbox', 'segm', 'PQ'],
+    metric=dict(PQ=51.9, bbox_mAP=44.8, segm_mAP=41.9),
+)
+mask_rcnn = dict(
+    config='configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.2, segm_mAP=34.7),
+)
+maskformer = dict(
+    config='configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py',
+    checkpoint='maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth', # noqa
+    eval='PQ',
+    metric=dict(PQ=46.9),
+)
+ms_rcnn = dict(
+    config='configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py',
+    checkpoint='ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.2, segm_mAP=36.0),
+)
+nas_fcos = dict(
+    config='configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py',  # noqa
+    checkpoint='nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=39.4),
+)
+nas_fpn = dict(
+    config='configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py',
+    checkpoint='retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.5),
+)
+paa = dict(
+    config='configs/paa/paa_r50_fpn_1x_coco.py',
+    checkpoint='paa_r50_fpn_1x_coco_20200821-936edec3.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+pafpn = dict(
+    config='configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py',
+    checkpoint='faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.5),
+)
+panoptic_fpn = dict(
+    config='configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py',
+    checkpoint='panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth', # noqa
+    eval='PQ',
+    metric=dict(PQ=40.2),
+)
+pisa = dict(
+    config='configs/pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py',
+    checkpoint='pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.4),
+)
+point_rend = dict(
+    config='configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py',
+    checkpoint='point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.4, segm_mAP=36.3),
+)
+pvt = dict(
+    config='configs/pvt/retinanet_pvt-s_fpn_1x_coco.py',
+    checkpoint='retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+queryinst = dict(
+    config='configs/queryinst/queryinst_r50_fpn_1x_coco.py',
+    checkpoint='queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=42.0, segm_mAP=37.5),
+)
+regnet = dict(
+    config='configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py',
+    checkpoint='mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.4, segm_mAP=36.7),
+)
+reppoints = dict(
+    config='configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py',
+    checkpoint='reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.0),
+)
+res2net = dict(
+    config='configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py',
+    checkpoint='faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=43.0),
+)
+resnest = dict(
+    config='configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py',  # noqa
+    checkpoint='faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=42.0),
+)
+resnet_strikes_back = dict(
+    config='configs/resnet_strikes_back/mask-rcnn_r50-rsb-pre_fpn_1x_coco.py', # noqa
+    checkpoint='mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=41.2, segm_mAP=38.2),
+)
+retinanet = dict(
+    config='configs/retinanet/retinanet_r50_fpn_1x_coco.py',
+    checkpoint='retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=36.5),
+)
+rpn = dict(
+    config='configs/rpn/rpn_r50_fpn_1x_coco.py',
+    checkpoint='rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth', # noqa
+    eval='proposal_fast',
+    metric=dict(AR_1000=58.2),
+)
+sabl = [
+    dict(
+        config='configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py',
+        checkpoint='sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth',
+        url='https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth', # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=37.7),
+    ),
+    dict(
+        config='configs/sabl/sabl-faster-rcnn_r50_fpn_1x_coco.py',
+        checkpoint='sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth',
+        url='https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth', # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=39.9),
+    ),
+]
+scnet = dict(
+    config='configs/scnet/scnet_r50_fpn_1x_coco.py',
+    checkpoint='scnet_r50_fpn_1x_coco-c3f09857.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=43.5),
+)
+scratch = dict(
+    config='configs/scratch/mask-rcnn_r50-scratch_fpn_gn-all_6x_coco.py',
+    checkpoint='scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=41.2, segm_mAP=37.4),
+)
+solo = dict(
+    config='configs/solo/decoupled-solo_r50_fpn_1x_coco.py',
+    checkpoint='decoupled_solo_r50_fpn_1x_coco_20210820_233348-6337c589.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_1x_coco/decoupled_solo_r50_fpn_1x_coco_20210820_233348-6337c589.pth', # noqa
+    eval='segm',
+    metric=dict(segm_mAP=33.9),
+)
+solov2 = dict(
+    config='configs/solov2/solov2_r50_fpn_1x_coco.py',
+    checkpoint='solov2_r50_fpn_1x_coco_20220512_125858-a357fa23.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_1x_coco/solov2_r50_fpn_1x_coco_20220512_125858-a357fa23.pth', # noqa
+    eval='segm',
+    metric=dict(segm_mAP=34.8),
+)
+sparse_rcnn = dict(
+    config='configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py',
+    checkpoint='sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.9),
+)
+ssd = [
+    dict(
+        config='configs/ssd/ssd300_coco.py',
+        checkpoint='ssd300_coco_20210803_015428-d231a06e.pth',
+        url='https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth', # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=25.5),
+    ),
+    dict(
+        config='configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py',
+        checkpoint='ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth',  # noqa
+        url='https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth', # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=21.3),
+    ),
+]
+swin = dict(
+    config='configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py',
+    checkpoint='mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth', # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=42.7, segm_mAP=39.3),
+)
+tood = dict(
+    config='configs/tood/tood_r50_fpn_1x_coco.py',
+    checkpoint='tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_1x_coco/tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=42.4),
+)
+tridentnet = dict(
+    config='configs/tridentnet/tridentnet_r50-caffe_1x_coco.py',
+    checkpoint='tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.6),
+)
+vfnet = dict(
+    config='configs/vfnet/vfnet_r50_fpn_1x_coco.py',
+    checkpoint='vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=41.6),
+)
+yolact = dict(
+    config='configs/yolact/yolact_r50_1xb8-55e_coco.py',
+    checkpoint='yolact_r50_1x8_coco_20200908-f38d58df.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth', # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=31.2, segm_mAP=29.0),
+)
+yolo = dict(
+    config='configs/yolo/yolov3_d53_8xb8-320-273e_coco.py',
+    checkpoint='yolov3_d53_320_273e_coco-421362b6.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=27.9),
+)
+yolof = dict(
+    config='configs/yolof/yolof_r50-c5_8xb8-1x_coco.py',
+    checkpoint='yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth',
+    url='https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.5),
+)
+yolox = dict(
+    config='configs/yolox/yolox_tiny_8xb8-300e_coco.py',
+    checkpoint='yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth',  # noqa
+    url='https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth', # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=31.8),
+)
+# yapf: enable

mmdetection/.dev_scripts/batch_train_list.txt

@@ -0,0 +1,80 @@
+configs/albu_example/mask-rcnn_r50_fpn_albu_1x_coco.py
+configs/atss/atss_r50_fpn_1x_coco.py
+configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py
+configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py
+configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py
+configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py
+configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py
+configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py
+configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py
+configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py
+configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py
+configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py
+configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py
+configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py
+configs/ddod/ddod_r50_fpn_1x_coco.py
+configs/detectors/detectors_htc-r50_1x_coco.py
+configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py
+configs/detr/detr_r50_8xb2-150e_coco.py
+configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py
+configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py
+configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py
+configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py
+configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py
+configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py
+configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py
+configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py
+configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py
+configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py
+configs/free_anchor/freeanchor_r50_fpn_1x_coco.py
+configs/fsaf/fsaf_r50_fpn_1x_coco.py
+configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py
+configs/gfl/gfl_r50_fpn_1x_coco.py
+configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py
+configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py
+configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py
+configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py
+configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py
+configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py
+configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py
+configs/htc/htc_r50_fpn_1x_coco.py
+configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py
+configs/lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py
+configs/ld/ld_r18-gflv1-r101_fpn_1x_coco.py
+configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py
+configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
+configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py
+configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py
+configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py
+configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py
+configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py
+configs/paa/paa_r50_fpn_1x_coco.py
+configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py
+configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py
+configs/pisa/mask-rcnn_r50_fpn_pisa_1x_coco.py
+configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py
+configs/pvt/retinanet_pvt-t_fpn_1x_coco.py
+configs/queryinst/queryinst_r50_fpn_1x_coco.py
+configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
+configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py
+configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py
+configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py
+configs/resnet_strikes_back/retinanet_r50-rsb-pre_fpn_1x_coco.py
+configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py
+configs/rpn/rpn_r50_fpn_1x_coco.py
+configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py
+configs/scnet/scnet_r50_fpn_1x_coco.py
+configs/scratch/faster-rcnn_r50-scratch_fpn_gn-all_6x_coco.py
+configs/solo/solo_r50_fpn_1x_coco.py
+configs/solov2/solov2_r50_fpn_1x_coco.py
+configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py
+configs/ssd/ssd300_coco.py
+configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py
+configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py
+configs/tood/tood_r50_fpn_1x_coco.py
+'configs/tridentnet/tridentnet_r50-caffe_1x_coco.py
+configs/vfnet/vfnet_r50_fpn_1x_coco.py
+configs/yolact/yolact_r50_8xb8-55e_coco.py
+configs/yolo/yolov3_d53_8xb8-320-273e_coco.py
+configs/yolof/yolof_r50-c5_8xb8-1x_coco.py
+configs/yolox/yolox_tiny_8xb8-300e_coco.py

mmdetection/.dev_scripts/benchmark_filter.py

@@ -0,0 +1,167 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Filter configs to train')
+    parser.add_argument(
+        '--basic-arch',
+        action='store_true',
+        help='to train models in basic arch')
+    parser.add_argument(
+        '--datasets', action='store_true', help='to train models in dataset')
+    parser.add_argument(
+        '--data-pipeline',
+        action='store_true',
+        help='to train models related to data pipeline, e.g. augmentations')
+    parser.add_argument(
+        '--nn-module',
+        action='store_true',
+        help='to train models related to neural network modules')
+    parser.add_argument(
+        '--model-options',
+        nargs='+',
+        help='custom options to special model benchmark')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='batch_train_list.txt',
+        help='output path of gathered metrics to be stored')
+    args = parser.parse_args()
+    return args
+
+
+basic_arch_root = [
+    'atss', 'autoassign', 'cascade_rcnn', 'cascade_rpn', 'centripetalnet',
+    'cornernet', 'detectors', 'deformable_detr', 'detr', 'double_heads',
+    'dynamic_rcnn', 'faster_rcnn', 'fcos', 'foveabox', 'fp16', 'free_anchor',
+    'fsaf', 'gfl', 'ghm', 'grid_rcnn', 'guided_anchoring', 'htc', 'ld',
+    'libra_rcnn', 'mask_rcnn', 'ms_rcnn', 'nas_fcos', 'paa', 'pisa',
+    'point_rend', 'reppoints', 'retinanet', 'rpn', 'sabl', 'ssd', 'tridentnet',
+    'vfnet', 'yolact', 'yolo', 'sparse_rcnn', 'scnet', 'yolof', 'centernet'
+]
+
+datasets_root = [
+    'wider_face', 'pascal_voc', 'cityscapes', 'lvis', 'deepfashion'
+]
+
+data_pipeline_root = ['albu_example', 'instaboost']
+
+nn_module_root = [
+    'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn', 'gn+ws', 'hrnet',
+    'pafpn', 'nas_fpn', 'regnet', 'resnest', 'res2net', 'groie'
+]
+
+benchmark_pool = [
+    'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py',
+    'configs/atss/atss_r50_fpn_1x_coco.py',
+    'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py',
+    'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py',
+    'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+    'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py',
+    'configs/centripetalnet/'
+    'centripetalnet_hourglass104_mstest_16x6_210e_coco.py',
+    'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
+    'configs/cornernet/'
+    'cornernet_hourglass104_mstest_8x6_210e_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py',
+    'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py',
+    'configs/detectors/detectors_htc_r50_1x_coco.py',
+    'configs/detr/detr_r50_8x2_150e_coco.py',
+    'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py',
+    'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py',  # noqa
+    'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py',
+    'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py',
+    'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
+    'configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py',
+    'configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py',
+    'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
+    'configs/fsaf/fsaf_r50_fpn_1x_coco.py',
+    'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py',
+    'configs/gfl/gfl_r50_fpn_1x_coco.py',
+    'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py',
+    'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py',
+    'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py',
+    'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
+    'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py',
+    'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py',
+    'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py',
+    'configs/htc/htc_r50_fpn_1x_coco.py',
+    'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py',
+    'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py',
+    'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py',
+    'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py',
+    'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py',
+    'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py',
+    'configs/paa/paa_r50_fpn_1x_coco.py',
+    'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py',
+    'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py',
+    'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py',
+    'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py',
+    'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py',
+    'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py',
+    'configs/resnest/'
+    'mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py',
+    'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py',
+    'configs/rpn/rpn_r50_fpn_1x_coco.py',
+    'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py',
+    'configs/ssd/ssd300_coco.py',
+    'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py',
+    'configs/vfnet/vfnet_r50_fpn_1x_coco.py',
+    'configs/yolact/yolact_r50_1x8_coco.py',
+    'configs/yolo/yolov3_d53_320_273e_coco.py',
+    'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py',
+    'configs/scnet/scnet_r50_fpn_1x_coco.py',
+    'configs/yolof/yolof_r50_c5_8x8_1x_coco.py',
+]
+
+
+def main():
+    args = parse_args()
+
+    benchmark_type = []
+    if args.basic_arch:
+        benchmark_type += basic_arch_root
+    if args.datasets:
+        benchmark_type += datasets_root
+    if args.data_pipeline:
+        benchmark_type += data_pipeline_root
+    if args.nn_module:
+        benchmark_type += nn_module_root
+
+    special_model = args.model_options
+    if special_model is not None:
+        benchmark_type += special_model
+
+    config_dpath = 'configs/'
+    benchmark_configs = []
+    for cfg_root in benchmark_type:
+        cfg_dir = osp.join(config_dpath, cfg_root)
+        configs = os.scandir(cfg_dir)
+        for cfg in configs:
+            config_path = osp.join(cfg_dir, cfg.name)
+            if (config_path in benchmark_pool
+                    and config_path not in benchmark_configs):
+                benchmark_configs.append(config_path)
+
+    print(f'Totally found {len(benchmark_configs)} configs to benchmark')
+    with open(args.out, 'w') as f:
+        for config in benchmark_configs:
+            f.write(config + '\n')
+
+
+if __name__ == '__main__':
+    main()

mmdetection/.dev_scripts/benchmark_full_models.txt

@@ -0,0 +1,96 @@
+albu_example/mask-rcnn_r50_fpn_albu_1x_coco.py
+atss/atss_r50_fpn_1x_coco.py
+autoassign/autoassign_r50-caffe_fpn_1x_coco.py
+boxinst/boxinst_r50_fpn_ms-90k_coco.py
+carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py
+cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py
+cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py
+cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py
+centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py
+centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py
+condinst/condinst_r50_fpn_ms-poly-90k_coco_instance.py
+conditional_detr/conditional-detr_r50_8xb2-50e_coco.py
+convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py
+cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py
+dab_detr/dab-detr_r50_8xb2-50e_coco.py
+dcn/mask-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py
+dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py
+ddod/ddod_r50_fpn_1x_coco.py
+deformable_detr/deformable-detr_r50_16xb2-50e_coco.py
+detectors/detectors_htc-r50_1x_coco.py
+detr/detr_r50_8xb2-150e_coco.py
+dino/dino-4scale_r50_8xb2-12e_coco.py
+double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py
+dyhead/atss_r50_fpn_dyhead_1x_coco.py
+dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py
+efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py
+empirical_attention/faster-rcnn_r50-attn0010-dcn_fpn_1x_coco.py
+faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py
+fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py
+foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py
+fpg/retinanet_r50_fpg_crop640_50e_coco.py
+free_anchor/freeanchor_r50_fpn_1x_coco.py
+fsaf/fsaf_r50_fpn_1x_coco.py
+gcnet/mask-rcnn_r50-gcb-r4-c3-c5_fpn_1x_coco.py
+gfl/gfl_r50_fpn_1x_coco.py
+glip/glip_atss_swin-t_a_fpn_dyhead_pretrain_obj365.py
+ghm/retinanet_r50_fpn_ghm-1x_coco.py
+gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py
+gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py
+grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py
+groie/faste-rcnn_r50_fpn_groie_1x_coco.py
+guided_anchoring/ga-faster-rcnn_r50-caffe_fpn_1x_coco.py
+hrnet/htc_hrnetv2p-w18_20e_coco.py
+htc/htc_r50_fpn_1x_coco.py
+instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py
+lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py
+ld/ld_r18-gflv1-r101_fpn_1x_coco.py
+libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py
+lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py
+mask2former/mask2former_r50_8xb2-lsj-50e_coco.py
+mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
+mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py
+maskformer/maskformer_r50_ms-16xb1-75e_coco.py
+ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py
+nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py
+nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py
+paa/paa_r50_fpn_1x_coco.py
+pafpn/faster-rcnn_r50_pafpn_1x_coco.py
+panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py
+pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py
+point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py
+pvt/retinanet_pvtv2-b0_fpn_1x_coco.py
+queryinst/queryinst_r50_fpn_1x_coco.py
+regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py
+reppoints/reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py
+res2net/faster-rcnn_res2net-101_fpn_2x_coco.py
+resnest/mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py
+resnet_strikes_back/faster-rcnn_r50-rsb-pre_fpn_1x_coco.py
+retinanet/retinanet_r50_fpn_1x_coco.py
+rpn/rpn_r50_fpn_1x_coco.py
+rtmdet/rtmdet_s_8xb32-300e_coco.py
+rtmdet/rtmdet-ins_s_8xb32-300e_coco.py
+sabl/sabl-retinanet_r50_fpn_1x_coco.py
+scnet/scnet_r50_fpn_1x_coco.py
+scratch/faster-rcnn_r50-scratch_fpn_gn-all_6x_coco.py
+seesaw_loss/mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py
+simple_copy_paste/mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-90k_coco.py
+soft_teacher/soft-teacher_faster-rcnn_r50-caffe_fpn_180k_semi-0.1-coco.py
+solo/solo_r50_fpn_1x_coco.py
+solov2/solov2_r50_fpn_1x_coco.py
+sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py
+ssd/ssd300_coco.py
+swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py
+tood/tood_r50_fpn_1x_coco.py
+tridentnet/tridentnet_r50-caffe_1x_coco.py
+vfnet/vfnet_r50_fpn_1x_coco.py
+yolact/yolact_r50_8xb8-55e_coco.py
+yolo/yolov3_d53_8xb8-320-273e_coco.py
+yolof/yolof_r50-c5_8xb8-1x_coco.py
+yolox/yolox_s_8xb8-300e_coco.py
+deepsort/deepsort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py
+mask2former_vis/mask2former_r50_8xb2-8e_youtubevis2021.py
+masktrack_rcnn/masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2021.py
+ocsort/ocsort_yolox_x_8xb4-amp-80e_crowdhuman-mot17halftrain_test-mot17halfval.py
+qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py
+strongsort/strongsort_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py

mmdetection/.dev_scripts/benchmark_inference_fps.py

@@ -0,0 +1,171 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+from mmengine.config import Config, DictAction
+from mmengine.dist import init_dist
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist
+from terminaltables import GithubFlavoredMarkdownTable
+
+from tools.analysis_tools.benchmark import repeat_measure_inference_speed
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet benchmark a model of FPS')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument(
+        '--round-num',
+        type=int,
+        default=1,
+        help='round a number to a given precision in decimal digits')
+    parser.add_argument(
+        '--repeat-num',
+        type=int,
+        default=1,
+        help='number of repeat times of measurement for averaging the results')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered fps to be stored')
+    parser.add_argument(
+        '--max-iter', type=int, default=2000, help='num of max iter')
+    parser.add_argument(
+        '--log-interval', type=int, default=50, help='interval of logging')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def results2markdown(result_dict):
+    table_data = []
+    is_multiple_results = False
+    for cfg_name, value in result_dict.items():
+        name = cfg_name.replace('configs/', '')
+        fps = value['fps']
+        ms_times_pre_image = value['ms_times_pre_image']
+        if isinstance(fps, list):
+            is_multiple_results = True
+            mean_fps = value['mean_fps']
+            mean_times_pre_image = value['mean_times_pre_image']
+            fps_str = ','.join([str(s) for s in fps])
+            ms_times_pre_image_str = ','.join(
+                [str(s) for s in ms_times_pre_image])
+            table_data.append([
+                name, fps_str, mean_fps, ms_times_pre_image_str,
+                mean_times_pre_image
+            ])
+        else:
+            table_data.append([name, fps, ms_times_pre_image])
+
+    if is_multiple_results:
+        table_data.insert(0, [
+            'model', 'fps', 'mean_fps', 'times_pre_image(ms)',
+            'mean_times_pre_image(ms)'
+        ])
+
+    else:
+        table_data.insert(0, ['model', 'fps', 'times_pre_image(ms)'])
+    table = GithubFlavoredMarkdownTable(table_data)
+    print(table.table, flush=True)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    assert args.round_num >= 0
+    assert args.repeat_num >= 1
+
+    config = Config.fromfile(args.config)
+
+    if args.launcher == 'none':
+        raise NotImplementedError('Only supports distributed mode')
+    else:
+        init_dist(args.launcher)
+
+    result_dict = {}
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            record_metrics = model_info['metric']
+            cfg_path = model_info['config'].strip()
+            cfg = Config.fromfile(cfg_path)
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            try:
+                fps = repeat_measure_inference_speed(cfg, checkpoint,
+                                                     args.max_iter,
+                                                     args.log_interval,
+                                                     args.fuse_conv_bn,
+                                                     args.repeat_num)
+                if args.repeat_num > 1:
+                    fps_list = [round(fps_, args.round_num) for fps_ in fps]
+                    times_pre_image_list = [
+                        round(1000 / fps_, args.round_num) for fps_ in fps
+                    ]
+                    mean_fps = round(
+                        sum(fps_list) / len(fps_list), args.round_num)
+                    mean_times_pre_image = round(
+                        sum(times_pre_image_list) / len(times_pre_image_list),
+                        args.round_num)
+                    print(
+                        f'{cfg_path} '
+                        f'Overall fps: {fps_list}[{mean_fps}] img / s, '
+                        f'times per image: '
+                        f'{times_pre_image_list}[{mean_times_pre_image}] '
+                        f'ms / img',
+                        flush=True)
+                    result_dict[cfg_path] = dict(
+                        fps=fps_list,
+                        mean_fps=mean_fps,
+                        ms_times_pre_image=times_pre_image_list,
+                        mean_times_pre_image=mean_times_pre_image)
+                else:
+                    print(
+                        f'{cfg_path} fps : {fps:.{args.round_num}f} img / s, '
+                        f'times per image: {1000 / fps:.{args.round_num}f} '
+                        f'ms / img',
+                        flush=True)
+                    result_dict[cfg_path] = dict(
+                        fps=round(fps, args.round_num),
+                        ms_times_pre_image=round(1000 / fps, args.round_num))
+            except Exception as e:
+                print(f'{cfg_path} error: {repr(e)}')
+                if args.repeat_num > 1:
+                    result_dict[cfg_path] = dict(
+                        fps=[0],
+                        mean_fps=0,
+                        ms_times_pre_image=[0],
+                        mean_times_pre_image=0)
+                else:
+                    result_dict[cfg_path] = dict(fps=0, ms_times_pre_image=0)
+
+    if args.out:
+        mkdir_or_exist(args.out)
+        dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
+
+    results2markdown(result_dict)

mmdetection/.dev_scripts/benchmark_options.py

@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+third_part_libs = [
+    'pip install -r ../requirements/albu.txt',
+    'pip install instaboostfast',
+    'pip install git+https://github.com/cocodataset/panopticapi.git',
+    'pip install timm',
+    'pip install mmpretrain',
+    'pip install git+https://github.com/lvis-dataset/lvis-api.git',
+    'pip install -r ../requirements/multimodal.txt',
+    'pip install -r ../requirements/tracking.txt',
+    'pip install git+https://github.com/JonathonLuiten/TrackEval.git',
+]
+
+default_floating_range = 0.5
+model_floating_ranges = {'atss/atss_r50_fpn_1x_coco.py': 0.3}

mmdetection/.dev_scripts/benchmark_test.py

@@ -0,0 +1,115 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os
+import os.path as osp
+from argparse import ArgumentParser
+
+from mmengine.config import Config, DictAction
+from mmengine.logging import MMLogger
+from mmengine.registry import RUNNERS
+from mmengine.runner import Runner
+
+from mmdet.testing import replace_to_ceph
+from mmdet.utils import register_all_modules, replace_cfg_vals
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument('--work-dir', help='the dir to save logs')
+    parser.add_argument('--ceph', action='store_true')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    args = parser.parse_args()
+    return args
+
+
+# TODO: Need to refactor test.py so that it can be reused.
+def fast_test_model(config_name, checkpoint, args, logger=None):
+    cfg = Config.fromfile(config_name)
+    cfg = replace_cfg_vals(cfg)
+    cfg.launcher = args.launcher
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    # work_dir is determined in this priority: CLI > segment in file > filename
+    if args.work_dir is not None:
+        # update configs according to CLI args if args.work_dir is not None
+        cfg.work_dir = osp.join(args.work_dir,
+                                osp.splitext(osp.basename(config_name))[0])
+    elif cfg.get('work_dir', None) is None:
+        # use config filename as default work_dir if cfg.work_dir is None
+        cfg.work_dir = osp.join('./work_dirs',
+                                osp.splitext(osp.basename(config_name))[0])
+
+    if args.ceph:
+        replace_to_ceph(cfg)
+
+    cfg.load_from = checkpoint
+
+    # TODO: temporary plan
+    if 'visualizer' in cfg:
+        if 'name' in cfg.visualizer:
+            del cfg.visualizer.name
+
+    # build the runner from config
+    if 'runner_type' not in cfg:
+        # build the default runner
+        runner = Runner.from_cfg(cfg)
+    else:
+        # build customized runner from the registry
+        # if 'runner_type' is set in the cfg
+        runner = RUNNERS.build(cfg)
+
+    runner.test()
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    # register all modules in mmdet into the registries
+    register_all_modules(init_default_scope=False)
+
+    config = Config.fromfile(args.config)
+
+    # test all model
+    logger = MMLogger.get_instance(
+        name='MMLogger',
+        log_file='benchmark_test.log',
+        log_level=logging.ERROR)
+
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'], flush=True)
+            config_name = model_info['config'].strip()
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            try:
+                fast_test_model(config_name, checkpoint, args, logger)
+            except Exception as e:
+                logger.error(f'{config_name} " : {repr(e)}')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

mmdetection/.dev_scripts/benchmark_test_image.py

@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os.path as osp
+from argparse import ArgumentParser
+
+import mmcv
+from mmengine.config import Config
+from mmengine.logging import MMLogger
+from mmengine.utils import mkdir_or_exist
+
+from mmdet.apis import inference_detector, init_detector
+from mmdet.registry import VISUALIZERS
+from mmdet.utils import register_all_modules
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument('--img', default='demo/demo.jpg', help='Image file')
+    parser.add_argument('--aug', action='store_true', help='aug test')
+    parser.add_argument('--model-name', help='model name to inference')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument('--out-dir', default=None, help='Dir to output file')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='the interval of show (s), 0 is block')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--palette',
+        default='coco',
+        choices=['coco', 'voc', 'citys', 'random'],
+        help='Color palette used for visualization')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+def inference_model(config_name, checkpoint, visualizer, args, logger=None):
+    cfg = Config.fromfile(config_name)
+    if args.aug:
+        raise NotImplementedError()
+
+    model = init_detector(
+        cfg, checkpoint, palette=args.palette, device=args.device)
+    visualizer.dataset_meta = model.dataset_meta
+
+    # test a single image
+    result = inference_detector(model, args.img)
+
+    # show the results
+    if args.show or args.out_dir is not None:
+        img = mmcv.imread(args.img)
+        img = mmcv.imconvert(img, 'bgr', 'rgb')
+        out_file = None
+        if args.out_dir is not None:
+            out_dir = args.out_dir
+            mkdir_or_exist(out_dir)
+
+            out_file = osp.join(
+                out_dir,
+                config_name.split('/')[-1].replace('py', 'jpg'))
+
+        visualizer.add_datasample(
+            'result',
+            img,
+            data_sample=result,
+            draw_gt=False,
+            show=args.show,
+            wait_time=args.wait_time,
+            out_file=out_file,
+            pred_score_thr=args.score_thr)
+
+    return result
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    # register all modules in mmdet into the registries
+    register_all_modules()
+
+    config = Config.fromfile(args.config)
+
+    # init visualizer
+    visualizer_cfg = dict(type='DetLocalVisualizer', name='visualizer')
+    visualizer = VISUALIZERS.build(visualizer_cfg)
+
+    # test single model
+    if args.model_name:
+        if args.model_name in config:
+            model_infos = config[args.model_name]
+            if not isinstance(model_infos, list):
+                model_infos = [model_infos]
+            model_info = model_infos[0]
+            config_name = model_info['config'].strip()
+            print(f'processing: {config_name}', flush=True)
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            # build the model from a config file and a checkpoint file
+            inference_model(config_name, checkpoint, visualizer, args)
+            return
+        else:
+            raise RuntimeError('model name input error.')
+
+    # test all model
+    logger = MMLogger.get_instance(
+        name='MMLogger',
+        log_file='benchmark_test_image.log',
+        log_level=logging.ERROR)
+
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'], flush=True)
+            config_name = model_info['config'].strip()
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            try:
+                # build the model from a config file and a checkpoint file
+                inference_model(config_name, checkpoint, visualizer, args,
+                                logger)
+            except Exception as e:
+                logger.error(f'{config_name} " : {repr(e)}')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

mmdetection/.dev_scripts/benchmark_train.py

@@ -0,0 +1,178 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os
+import os.path as osp
+from argparse import ArgumentParser
+
+from mmengine.config import Config, DictAction
+from mmengine.logging import MMLogger, print_log
+from mmengine.registry import RUNNERS
+from mmengine.runner import Runner
+
+from mmdet.testing import replace_to_ceph
+from mmdet.utils import register_all_modules, replace_cfg_vals
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('--work-dir', help='the dir to save logs and models')
+    parser.add_argument('--ceph', action='store_true')
+    parser.add_argument('--save-ckpt', action='store_true')
+    parser.add_argument(
+        '--amp',
+        action='store_true',
+        default=False,
+        help='enable automatic-mixed-precision training')
+    parser.add_argument(
+        '--auto-scale-lr',
+        action='store_true',
+        help='enable automatically scaling LR.')
+    parser.add_argument(
+        '--resume',
+        action='store_true',
+        help='resume from the latest checkpoint in the work_dir automatically')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    args = parser.parse_args()
+    return args
+
+
+# TODO: Need to refactor train.py so that it can be reused.
+def fast_train_model(config_name, args, logger=None):
+    cfg = Config.fromfile(config_name)
+    cfg = replace_cfg_vals(cfg)
+    cfg.launcher = args.launcher
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    # work_dir is determined in this priority: CLI > segment in file > filename
+    if args.work_dir is not None:
+        # update configs according to CLI args if args.work_dir is not None
+        cfg.work_dir = osp.join(args.work_dir,
+                                osp.splitext(osp.basename(config_name))[0])
+    elif cfg.get('work_dir', None) is None:
+        # use config filename as default work_dir if cfg.work_dir is None
+        cfg.work_dir = osp.join('./work_dirs',
+                                osp.splitext(osp.basename(config_name))[0])
+
+    ckpt_hook = cfg.default_hooks.checkpoint
+    by_epoch = ckpt_hook.get('by_epoch', True)
+    fast_stop_hook = dict(type='FastStopTrainingHook')
+    fast_stop_hook['by_epoch'] = by_epoch
+    if args.save_ckpt:
+        if by_epoch:
+            interval = 1
+            stop_iter_or_epoch = 2
+        else:
+            interval = 4
+            stop_iter_or_epoch = 10
+        fast_stop_hook['stop_iter_or_epoch'] = stop_iter_or_epoch
+        fast_stop_hook['save_ckpt'] = True
+        ckpt_hook.interval = interval
+
+    if 'custom_hooks' in cfg:
+        cfg.custom_hooks.append(fast_stop_hook)
+    else:
+        custom_hooks = [fast_stop_hook]
+        cfg.custom_hooks = custom_hooks
+
+    # TODO: temporary plan
+    if 'visualizer' in cfg:
+        if 'name' in cfg.visualizer:
+            del cfg.visualizer.name
+
+    # enable automatic-mixed-precision training
+    if args.amp is True:
+        optim_wrapper = cfg.optim_wrapper.type
+        if optim_wrapper == 'AmpOptimWrapper':
+            print_log(
+                'AMP training is already enabled in your config.',
+                logger='current',
+                level=logging.WARNING)
+        else:
+            assert optim_wrapper == 'OptimWrapper', (
+                '`--amp` is only supported when the optimizer wrapper type is '
+                f'`OptimWrapper` but got {optim_wrapper}.')
+            cfg.optim_wrapper.type = 'AmpOptimWrapper'
+            cfg.optim_wrapper.loss_scale = 'dynamic'
+
+    # enable automatically scaling LR
+    if args.auto_scale_lr:
+        if 'auto_scale_lr' in cfg and \
+                'enable' in cfg.auto_scale_lr and \
+                'base_batch_size' in cfg.auto_scale_lr:
+            cfg.auto_scale_lr.enable = True
+        else:
+            raise RuntimeError('Can not find "auto_scale_lr" or '
+                               '"auto_scale_lr.enable" or '
+                               '"auto_scale_lr.base_batch_size" in your'
+                               ' configuration file.')
+
+    if args.ceph:
+        replace_to_ceph(cfg)
+
+    cfg.resume = args.resume
+
+    # build the runner from config
+    if 'runner_type' not in cfg:
+        # build the default runner
+        runner = Runner.from_cfg(cfg)
+    else:
+        # build customized runner from the registry
+        # if 'runner_type' is set in the cfg
+        runner = RUNNERS.build(cfg)
+
+    runner.train()
+
+
+# Sample test whether the train code is correct
+def main(args):
+    # register all modules in mmdet into the registries
+    register_all_modules(init_default_scope=False)
+
+    config = Config.fromfile(args.config)
+
+    # test all model
+    logger = MMLogger.get_instance(
+        name='MMLogger',
+        log_file='benchmark_train.log',
+        log_level=logging.ERROR)
+
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'], flush=True)
+            config_name = model_info['config'].strip()
+            try:
+                fast_train_model(config_name, args, logger)
+            except RuntimeError as e:
+                # quick exit is the normal exit message
+                if 'quick exit' not in repr(e):
+                    logger.error(f'{config_name} " : {repr(e)}')
+            except Exception as e:
+                logger.error(f'{config_name} " : {repr(e)}')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

mmdetection/.dev_scripts/benchmark_train_models.txt

@@ -0,0 +1,20 @@
+atss/atss_r50_fpn_1x_coco.py
+faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py
+mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py
+cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py
+panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py
+retinanet/retinanet_r50_fpn_1x_coco.py
+rtmdet/rtmdet_s_8xb32-300e_coco.py
+rtmdet/rtmdet-ins_s_8xb32-300e_coco.py
+deformable_detr/deformable-detr_r50_16xb2-50e_coco.py
+fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py
+centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py
+dino/dino-4scale_r50_8xb2-12e_coco.py
+htc/htc_r50_fpn_1x_coco.py
+mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
+swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py
+condinst/condinst_r50_fpn_ms-poly-90k_coco_instance.py
+lvis/mask-rcnn_r50_fpn_sample1e-3_ms-1x_lvis-v1.py
+mask2former_vis/mask2former_r50_8xb2-8e_youtubevis2021.py
+masktrack_rcnn/masktrack-rcnn_mask-rcnn_r50_fpn_8xb1-12e_youtubevis2021.py
+qdtrack/qdtrack_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain_test-mot17halfval.py

mmdetection/.dev_scripts/benchmark_valid_flops.py

@@ -0,0 +1,295 @@
+import logging
+import re
+import tempfile
+from argparse import ArgumentParser
+from collections import OrderedDict
+from functools import partial
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+from mmengine import Config, DictAction
+from mmengine.analysis import get_model_complexity_info
+from mmengine.analysis.print_helper import _format_size
+from mmengine.fileio import FileClient
+from mmengine.logging import MMLogger
+from mmengine.model import revert_sync_batchnorm
+from mmengine.runner import Runner
+from modelindex.load_model_index import load
+from rich.console import Console
+from rich.table import Table
+from rich.text import Text
+from tqdm import tqdm
+
+from mmdet.registry import MODELS
+from mmdet.utils import register_all_modules
+
+console = Console()
+MMDET_ROOT = Path(__file__).absolute().parents[1]
+
+
+def parse_args():
+    parser = ArgumentParser(description='Valid all models in model-index.yml')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[1280, 800],
+        help='input image size')
+    parser.add_argument(
+        '--checkpoint_root',
+        help='Checkpoint file root path. If set, load checkpoint before test.')
+    parser.add_argument('--img', default='demo/demo.jpg', help='Image file')
+    parser.add_argument('--models', nargs='+', help='models name to inference')
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=1,
+        help='The batch size during the inference.')
+    parser.add_argument(
+        '--flops', action='store_true', help='Get Flops and Params of models')
+    parser.add_argument(
+        '--flops-str',
+        action='store_true',
+        help='Output FLOPs and params counts in a string form.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--size_divisor',
+        type=int,
+        default=32,
+        help='Pad the input image, the minimum size that is divisible '
+        'by size_divisor, -1 means do not pad the image.')
+    args = parser.parse_args()
+    return args
+
+
+def inference(config_file, checkpoint, work_dir, args, exp_name):
+    logger = MMLogger.get_instance(name='MMLogger')
+    logger.warning('if you want test flops, please make sure torch>=1.12')
+    cfg = Config.fromfile(config_file)
+    cfg.work_dir = work_dir
+    cfg.load_from = checkpoint
+    cfg.log_level = 'WARN'
+    cfg.experiment_name = exp_name
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    # forward the model
+    result = {'model': config_file.stem}
+
+    if args.flops:
+
+        if len(args.shape) == 1:
+            h = w = args.shape[0]
+        elif len(args.shape) == 2:
+            h, w = args.shape
+        else:
+            raise ValueError('invalid input shape')
+        divisor = args.size_divisor
+        if divisor > 0:
+            h = int(np.ceil(h / divisor)) * divisor
+            w = int(np.ceil(w / divisor)) * divisor
+
+        input_shape = (3, h, w)
+        result['resolution'] = input_shape
+
+        try:
+            cfg = Config.fromfile(config_file)
+            if hasattr(cfg, 'head_norm_cfg'):
+                cfg['head_norm_cfg'] = dict(type='SyncBN', requires_grad=True)
+                cfg['model']['roi_head']['bbox_head']['norm_cfg'] = dict(
+                    type='SyncBN', requires_grad=True)
+                cfg['model']['roi_head']['mask_head']['norm_cfg'] = dict(
+                    type='SyncBN', requires_grad=True)
+
+            if args.cfg_options is not None:
+                cfg.merge_from_dict(args.cfg_options)
+
+            model = MODELS.build(cfg.model)
+            input = torch.rand(1, *input_shape)
+            if torch.cuda.is_available():
+                model.cuda()
+                input = input.cuda()
+            model = revert_sync_batchnorm(model)
+            inputs = (input, )
+            model.eval()
+            outputs = get_model_complexity_info(
+                model, input_shape, inputs, show_table=False, show_arch=False)
+            flops = outputs['flops']
+            params = outputs['params']
+            activations = outputs['activations']
+            result['Get Types'] = 'direct'
+        except:  # noqa 772
+            logger = MMLogger.get_instance(name='MMLogger')
+            logger.warning(
+                'Direct get flops failed, try to get flops with data')
+            cfg = Config.fromfile(config_file)
+            if hasattr(cfg, 'head_norm_cfg'):
+                cfg['head_norm_cfg'] = dict(type='SyncBN', requires_grad=True)
+                cfg['model']['roi_head']['bbox_head']['norm_cfg'] = dict(
+                    type='SyncBN', requires_grad=True)
+                cfg['model']['roi_head']['mask_head']['norm_cfg'] = dict(
+                    type='SyncBN', requires_grad=True)
+            data_loader = Runner.build_dataloader(cfg.val_dataloader)
+            data_batch = next(iter(data_loader))
+            model = MODELS.build(cfg.model)
+            if torch.cuda.is_available():
+                model = model.cuda()
+            model = revert_sync_batchnorm(model)
+            model.eval()
+            _forward = model.forward
+            data = model.data_preprocessor(data_batch)
+            del data_loader
+            model.forward = partial(
+                _forward, data_samples=data['data_samples'])
+            outputs = get_model_complexity_info(
+                model,
+                input_shape,
+                data['inputs'],
+                show_table=False,
+                show_arch=False)
+            flops = outputs['flops']
+            params = outputs['params']
+            activations = outputs['activations']
+            result['Get Types'] = 'dataloader'
+
+        if args.flops_str:
+            flops = _format_size(flops)
+            params = _format_size(params)
+            activations = _format_size(activations)
+
+        result['flops'] = flops
+        result['params'] = params
+
+    return result
+
+
+def show_summary(summary_data, args):
+    table = Table(title='Validation Benchmark Regression Summary')
+    table.add_column('Model')
+    table.add_column('Validation')
+    table.add_column('Resolution (c, h, w)')
+    if args.flops:
+        table.add_column('Flops', justify='right', width=11)
+        table.add_column('Params', justify='right')
+
+    for model_name, summary in summary_data.items():
+        row = [model_name]
+        valid = summary['valid']
+        color = 'green' if valid == 'PASS' else 'red'
+        row.append(f'[{color}]{valid}[/{color}]')
+        if valid == 'PASS':
+            row.append(str(summary['resolution']))
+            if args.flops:
+                row.append(str(summary['flops']))
+                row.append(str(summary['params']))
+        table.add_row(*row)
+
+    console.print(table)
+    table_data = {
+        x.header: [Text.from_markup(y).plain for y in x.cells]
+        for x in table.columns
+    }
+    table_pd = pd.DataFrame(table_data)
+    table_pd.to_csv('./mmdetection_flops.csv')
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    register_all_modules()
+    model_index_file = MMDET_ROOT / 'model-index.yml'
+    model_index = load(str(model_index_file))
+    model_index.build_models_with_collections()
+    models = OrderedDict({model.name: model for model in model_index.models})
+
+    logger = MMLogger(
+        'validation',
+        logger_name='validation',
+        log_file='benchmark_test_image.log',
+        log_level=logging.INFO)
+
+    if args.models:
+        patterns = [
+            re.compile(pattern.replace('+', '_')) for pattern in args.models
+        ]
+        filter_models = {}
+        for k, v in models.items():
+            k = k.replace('+', '_')
+            if any([re.match(pattern, k) for pattern in patterns]):
+                filter_models[k] = v
+        if len(filter_models) == 0:
+            print('No model found, please specify models in:')
+            print('\n'.join(models.keys()))
+            return
+        models = filter_models
+
+    summary_data = {}
+    tmpdir = tempfile.TemporaryDirectory()
+    for model_name, model_info in tqdm(models.items()):
+
+        if model_info.config is None:
+            continue
+
+        model_info.config = model_info.config.replace('%2B', '+')
+        config = Path(model_info.config)
+
+        try:
+            config.exists()
+        except:  # noqa 722
+            logger.error(f'{model_name}: {config} not found.')
+            continue
+
+        logger.info(f'Processing: {model_name}')
+
+        http_prefix = 'https://download.openmmlab.com/mmdetection/'
+        if args.checkpoint_root is not None:
+            root = args.checkpoint_root
+            if 's3://' in args.checkpoint_root:
+                from petrel_client.common.exception import AccessDeniedError
+                file_client = FileClient.infer_client(uri=root)
+                checkpoint = file_client.join_path(
+                    root, model_info.weights[len(http_prefix):])
+                try:
+                    exists = file_client.exists(checkpoint)
+                except AccessDeniedError:
+                    exists = False
+            else:
+                checkpoint = Path(root) / model_info.weights[len(http_prefix):]
+                exists = checkpoint.exists()
+            if exists:
+                checkpoint = str(checkpoint)
+            else:
+                print(f'WARNING: {model_name}: {checkpoint} not found.')
+                checkpoint = None
+        else:
+            checkpoint = None
+
+        try:
+            # build the model from a config file and a checkpoint file
+            result = inference(MMDET_ROOT / config, checkpoint, tmpdir.name,
+                               args, model_name)
+            result['valid'] = 'PASS'
+        except Exception:  # noqa 722
+            import traceback
+            logger.error(f'"{config}" :\n{traceback.format_exc()}')
+            result = {'valid': 'FAIL'}
+
+        summary_data[model_name] = result
+
+    tmpdir.cleanup()
+    show_summary(summary_data, args)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)

mmdetection/.dev_scripts/check_links.py

@@ -0,0 +1,157 @@
+# Modified from:
+# https://github.com/allenai/allennlp/blob/main/scripts/check_links.py
+
+import argparse
+import logging
+import os
+import pathlib
+import re
+import sys
+from multiprocessing.dummy import Pool
+from typing import NamedTuple, Optional, Tuple
+
+import requests
+from mmengine.logging import MMLogger
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Goes through all the inline-links '
+        'in markdown files and reports the breakages')
+    parser.add_argument(
+        '--num-threads',
+        type=int,
+        default=100,
+        help='Number of processes to confirm the link')
+    parser.add_argument('--https-proxy', type=str, help='https proxy')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='link_reports.txt',
+        help='output path of reports')
+    args = parser.parse_args()
+    return args
+
+
+OK_STATUS_CODES = (
+    200,
+    401,  # the resource exists but may require some sort of login.
+    403,  # ^ same
+    405,  # HEAD method not allowed.
+    # the resource exists, but our default 'Accept-' header may not
+    # match what the server can provide.
+    406,
+)
+
+
+class MatchTuple(NamedTuple):
+    source: str
+    name: str
+    link: str
+
+
+def check_link(
+        match_tuple: MatchTuple,
+        http_session: requests.Session,
+        logger: logging = None) -> Tuple[MatchTuple, bool, Optional[str]]:
+    reason: Optional[str] = None
+    if match_tuple.link.startswith('http'):
+        result_ok, reason = check_url(match_tuple, http_session)
+    else:
+        result_ok = check_path(match_tuple)
+    if logger is None:
+        print(f"  {'✓' if result_ok else '✗'} {match_tuple.link}")
+    else:
+        logger.info(f"  {'✓' if result_ok else '✗'} {match_tuple.link}")
+    return match_tuple, result_ok, reason
+
+
+def check_url(match_tuple: MatchTuple,
+              http_session: requests.Session) -> Tuple[bool, str]:
+    """Check if a URL is reachable."""
+    try:
+        result = http_session.head(
+            match_tuple.link, timeout=5, allow_redirects=True)
+        return (
+            result.ok or result.status_code in OK_STATUS_CODES,
+            f'status code = {result.status_code}',
+        )
+    except (requests.ConnectionError, requests.Timeout):
+        return False, 'connection error'
+
+
+def check_path(match_tuple: MatchTuple) -> bool:
+    """Check if a file in this repository exists."""
+    relative_path = match_tuple.link.split('#')[0]
+    full_path = os.path.join(
+        os.path.dirname(str(match_tuple.source)), relative_path)
+    return os.path.exists(full_path)
+
+
+def main():
+    args = parse_args()
+
+    # setup logger
+    logger = MMLogger.get_instance(name='mmdet', log_file=args.out)
+
+    # setup https_proxy
+    if args.https_proxy:
+        os.environ['https_proxy'] = args.https_proxy
+
+    # setup http_session
+    http_session = requests.Session()
+    for resource_prefix in ('http://', 'https://'):
+        http_session.mount(
+            resource_prefix,
+            requests.adapters.HTTPAdapter(
+                max_retries=5,
+                pool_connections=20,
+                pool_maxsize=args.num_threads),
+        )
+
+    logger.info('Finding all markdown files in the current directory...')
+
+    project_root = (pathlib.Path(__file__).parent / '..').resolve()
+    markdown_files = project_root.glob('**/*.md')
+
+    all_matches = set()
+    url_regex = re.compile(r'\[([^!][^\]]+)\]\(([^)(]+)\)')
+    for markdown_file in markdown_files:
+        with open(markdown_file) as handle:
+            for line in handle.readlines():
+                matches = url_regex.findall(line)
+                for name, link in matches:
+                    if 'localhost' not in link:
+                        all_matches.add(
+                            MatchTuple(
+                                source=str(markdown_file),
+                                name=name,
+                                link=link))
+
+    logger.info(f'  {len(all_matches)} markdown files found')
+    logger.info('Checking to make sure we can retrieve each link...')
+
+    with Pool(processes=args.num_threads) as pool:
+        results = pool.starmap(check_link, [(match, http_session, logger)
+                                            for match in list(all_matches)])
+
+    # collect unreachable results
+    unreachable_results = [(match_tuple, reason)
+                           for match_tuple, success, reason in results
+                           if not success]
+
+    if unreachable_results:
+        logger.info('================================================')
+        logger.info(f'Unreachable links ({len(unreachable_results)}):')
+        for match_tuple, reason in unreachable_results:
+            logger.info('  > Source: ' + match_tuple.source)
+            logger.info('    Name: ' + match_tuple.name)
+            logger.info('    Link: ' + match_tuple.link)
+            if reason is not None:
+                logger.info('    Reason: ' + reason)
+        sys.exit(1)
+    logger.info('No Unreachable link found.')
+
+
+if __name__ == '__main__':
+    main()

mmdetection/.dev_scripts/convert_test_benchmark_script.py

@@ -0,0 +1,114 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+from mmengine import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark model list to script')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('--port', type=int, default=29666, help='dist port')
+    parser.add_argument(
+        '--run', action='store_true', help='run script directly')
+    parser.add_argument(
+        '--out', type=str, help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def process_model_info(model_info, work_dir):
+    config = model_info['config'].strip()
+    fname, _ = osp.splitext(osp.basename(config))
+    job_name = fname
+    work_dir = '$WORK_DIR/' + fname
+    checkpoint = model_info['checkpoint'].strip()
+    return dict(
+        config=config,
+        job_name=job_name,
+        work_dir=work_dir,
+        checkpoint=checkpoint)
+
+
+def create_test_bash_info(commands, model_test_dict, port, script_name,
+                          partition):
+    config = model_test_dict['config']
+    job_name = model_test_dict['job_name']
+    checkpoint = model_test_dict['checkpoint']
+    work_dir = model_test_dict['work_dir']
+
+    echo_info = f' \necho \'{config}\' &'
+    commands.append(echo_info)
+    commands.append('\n')
+
+    command_info = f'GPUS=8  GPUS_PER_NODE=8  ' \
+                   f'CPUS_PER_TASK=$CPUS_PRE_TASK {script_name} '
+
+    command_info += f'{partition} '
+    command_info += f'{job_name} '
+    command_info += f'{config} '
+    command_info += f'$CHECKPOINT_DIR/{checkpoint} '
+    command_info += f'--work-dir {work_dir} '
+
+    command_info += f'--cfg-option env_cfg.dist_cfg.port={port} '
+    command_info += ' &'
+
+    commands.append(command_info)
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+    assert args.out or args.run, \
+        ('Please specify at least one operation (save/run/ the '
+         'script) with the argument "--out" or "--run"')
+
+    commands = []
+    partition_name = 'PARTITION=$1 '
+    commands.append(partition_name)
+    commands.append('\n')
+
+    checkpoint_root = 'CHECKPOINT_DIR=$2 '
+    commands.append(checkpoint_root)
+    commands.append('\n')
+
+    work_dir = 'WORK_DIR=$3 '
+    commands.append(work_dir)
+    commands.append('\n')
+
+    cpus_pre_task = 'CPUS_PER_TASK=${4:-2} '
+    commands.append(cpus_pre_task)
+    commands.append('\n')
+
+    script_name = osp.join('tools', 'slurm_test.sh')
+    port = args.port
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'])
+            model_test_dict = process_model_info(model_info, work_dir)
+            create_test_bash_info(commands, model_test_dict, port, script_name,
+                                  '$PARTITION')
+            port += 1
+
+    command_str = ''.join(commands)
+    if args.out:
+        with open(args.out, 'w') as f:
+            f.write(command_str)
+    if args.run:
+        os.system(command_str)
+
+
+if __name__ == '__main__':
+    main()

mmdetection/.dev_scripts/convert_train_benchmark_script.py

@@ -0,0 +1,104 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark model json to script')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument(
+        '--run', action='store_true', help='run script directly')
+    parser.add_argument(
+        '--out', type=str, help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def determine_gpus(cfg_name):
+    gpus = 8
+    gpus_pre_node = 8
+
+    if cfg_name.find('16x') >= 0:
+        gpus = 16
+    elif cfg_name.find('4xb4') >= 0:
+        gpus = 4
+        gpus_pre_node = 4
+    elif 'lad' in cfg_name:
+        gpus = 2
+        gpus_pre_node = 2
+
+    return gpus, gpus_pre_node
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+    assert args.out or args.run, \
+        ('Please specify at least one operation (save/run/ the '
+         'script) with the argument "--out" or "--run"')
+
+    root_name = './tools'
+    train_script_name = osp.join(root_name, 'slurm_train.sh')
+
+    commands = []
+    partition_name = 'PARTITION=$1 '
+    commands.append(partition_name)
+    commands.append('\n')
+
+    work_dir = 'WORK_DIR=$2 '
+    commands.append(work_dir)
+    commands.append('\n')
+
+    cpus_pre_task = 'CPUS_PER_TASK=${3:-4} '
+    commands.append(cpus_pre_task)
+    commands.append('\n')
+    commands.append('\n')
+
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, cfg in enumerate(model_cfgs):
+            cfg = cfg.strip()
+            if len(cfg) == 0:
+                continue
+            # print cfg name
+            echo_info = f'echo \'{cfg}\' &'
+            commands.append(echo_info)
+            commands.append('\n')
+
+            fname, _ = osp.splitext(osp.basename(cfg))
+            out_fname = '$WORK_DIR/' + fname
+
+            gpus, gpus_pre_node = determine_gpus(cfg)
+            command_info = f'GPUS={gpus}  GPUS_PER_NODE={gpus_pre_node}  ' \
+                           f'CPUS_PER_TASK=$CPUS_PRE_TASK {train_script_name} '
+            command_info += '$PARTITION '
+            command_info += f'{fname} '
+            command_info += f'{cfg} '
+            command_info += f'{out_fname} '
+
+            command_info += '--cfg-options default_hooks.checkpoint.' \
+                            'max_keep_ckpts=1 '
+            command_info += '&'
+
+            commands.append(command_info)
+
+            if i < len(model_cfgs):
+                commands.append('\n')
+
+        command_str = ''.join(commands)
+        if args.out:
+            with open(args.out, 'w') as f:
+                f.write(command_str)
+        if args.run:
+            os.system(command_str)
+
+
+if __name__ == '__main__':
+    main()

mmdetection/.dev_scripts/covignore.cfg

@@ -0,0 +1,5 @@
+# Each line should be the relative path to the root directory
+# of this repo. Support regular expression as well.
+# For example:
+
+.*/__init__.py

mmdetection/.dev_scripts/diff_coverage_test.sh

@@ -0,0 +1,40 @@
+#!/bin/bash
+
+readarray -t IGNORED_FILES < $( dirname "$0" )/covignore.cfg
+REUSE_COVERAGE_REPORT=${REUSE_COVERAGE_REPORT:-0}
+REPO=${1:-"origin"}
+BRANCH=${2:-"refactor_dev"}
+
+git fetch $REPO $BRANCH
+
+PY_FILES=""
+for FILE_NAME in $(git diff --name-only ${REPO}/${BRANCH}); do
+    # Only test python files in mmdet/ existing in current branch, and not ignored in covignore.cfg
+    if [ ${FILE_NAME: -3} == ".py" ] && [ ${FILE_NAME:0:6} == "mmdet/" ] && [ -f "$FILE_NAME" ]; then
+        IGNORED=false
+        for IGNORED_FILE_NAME in "${IGNORED_FILES[@]}"; do
+            # Skip blank lines
+            if [ -z "$IGNORED_FILE_NAME" ]; then
+                continue
+            fi
+            if [ "${IGNORED_FILE_NAME::1}" != "#" ] && [[ "$FILE_NAME" =~ $IGNORED_FILE_NAME ]]; then
+                echo "Ignoring $FILE_NAME"
+                IGNORED=true
+                break
+            fi
+        done
+        if [ "$IGNORED" = false ]; then
+            PY_FILES="$PY_FILES $FILE_NAME"
+        fi
+    fi
+done
+
+# Only test the coverage when PY_FILES are not empty, otherwise they will test the entire project
+if [ ! -z "${PY_FILES}" ]
+then
+    if [ "$REUSE_COVERAGE_REPORT" == "0" ]; then
+        coverage run --branch --source mmdet -m pytest tests/
+    fi
+    coverage report --fail-under 80 -m $PY_FILES
+    interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 95 $PY_FILES
+fi

mmdetection/.dev_scripts/download_checkpoints.py

@@ -0,0 +1,83 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+import argparse
+import math
+import os
+import os.path as osp
+from multiprocessing import Pool
+
+import torch
+from mmengine.config import Config
+from mmengine.utils import mkdir_or_exist
+
+
+def download(url, out_file, min_bytes=math.pow(1024, 2), progress=True):
+    # math.pow(1024, 2) is mean 1 MB
+    assert_msg = f"Downloaded url '{url}' does not exist " \
+                 f'or size is < min_bytes={min_bytes}'
+    try:
+        print(f'Downloading {url} to {out_file}...')
+        torch.hub.download_url_to_file(url, str(out_file), progress=progress)
+        assert osp.exists(
+            out_file) and osp.getsize(out_file) > min_bytes, assert_msg
+    except Exception as e:
+        if osp.exists(out_file):
+            os.remove(out_file)
+        print(f'ERROR: {e}\nRe-attempting {url} to {out_file} ...')
+        os.system(f"curl -L '{url}' -o '{out_file}' --retry 3 -C -"
+                  )  # curl download, retry and resume on fail
+    finally:
+        if osp.exists(out_file) and osp.getsize(out_file) < min_bytes:
+            os.remove(out_file)  # remove partial downloads
+
+        if not osp.exists(out_file):
+            print(f'ERROR: {assert_msg}\n')
+        print('=========================================\n')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Download checkpoints')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'out', type=str, help='output dir of checkpoints to be stored')
+    parser.add_argument(
+        '--nproc', type=int, default=16, help='num of Processes')
+    parser.add_argument(
+        '--intranet',
+        action='store_true',
+        help='switch to internal network url')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    mkdir_or_exist(args.out)
+
+    cfg = Config.fromfile(args.config)
+
+    checkpoint_url_list = []
+    checkpoint_out_list = []
+
+    for model in cfg:
+        model_infos = cfg[model]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            checkpoint = model_info['checkpoint']
+            out_file = osp.join(args.out, checkpoint)
+            if not osp.exists(out_file):
+
+                url = model_info['url']
+                if args.intranet is True:
+                    url = url.replace('.com', '.sensetime.com')
+                    url = url.replace('https', 'http')
+
+                checkpoint_url_list.append(url)
+                checkpoint_out_list.append(out_file)
+
+    if len(checkpoint_url_list) > 0:
+        pool = Pool(min(os.cpu_count(), args.nproc))
+        pool.starmap(download, zip(checkpoint_url_list, checkpoint_out_list))
+    else:
+        print('No files to download!')

mmdetection/.dev_scripts/gather_models.py

@@ -0,0 +1,308 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os
+import os.path as osp
+import shutil
+import subprocess
+import time
+from collections import OrderedDict
+
+import torch
+import yaml
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist, scandir
+
+
+def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
+
+    class OrderedDumper(Dumper):
+        pass
+
+    def _dict_representer(dumper, data):
+        return dumper.represent_mapping(
+            yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items())
+
+    OrderedDumper.add_representer(OrderedDict, _dict_representer)
+    return yaml.dump(data, stream, OrderedDumper, **kwds)
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    if 'ema_state_dict' in checkpoint:
+        del checkpoint['ema_state_dict']
+
+    # remove ema state_dict
+    for key in list(checkpoint['state_dict']):
+        if key.startswith('ema_'):
+            checkpoint['state_dict'].pop(key)
+        elif key.startswith('data_preprocessor'):
+            checkpoint['state_dict'].pop(key)
+
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    subprocess.Popen(['mv', out_file, final_file])
+    return final_file
+
+
+def is_by_epoch(config):
+    cfg = Config.fromfile('./configs/' + config)
+    return cfg.train_cfg.type == 'EpochBasedTrainLoop'
+
+
+def get_final_epoch_or_iter(config):
+    cfg = Config.fromfile('./configs/' + config)
+    if cfg.train_cfg.type == 'EpochBasedTrainLoop':
+        return cfg.train_cfg.max_epochs
+    else:
+        return cfg.train_cfg.max_iters
+
+
+def get_best_epoch_or_iter(exp_dir):
+    best_epoch_iter_full_path = list(
+        sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
+    best_epoch_or_iter_model_path = best_epoch_iter_full_path.split('/')[-1]
+    best_epoch_or_iter = best_epoch_or_iter_model_path.\
+        split('_')[-1].split('.')[0]
+    return best_epoch_or_iter_model_path, int(best_epoch_or_iter)
+
+
+def get_real_epoch_or_iter(config):
+    cfg = Config.fromfile('./configs/' + config)
+    if cfg.train_cfg.type == 'EpochBasedTrainLoop':
+        epoch = cfg.train_cfg.max_epochs
+        return epoch
+    else:
+        return cfg.train_cfg.max_iters
+
+
+def get_final_results(log_json_path,
+                      epoch_or_iter,
+                      results_lut='coco/bbox_mAP',
+                      by_epoch=True):
+    result_dict = dict()
+    with open(log_json_path) as f:
+        r = f.readlines()[-1]
+        last_metric = r.split(',')[0].split(': ')[-1].strip()
+    result_dict[results_lut] = last_metric
+    return result_dict
+
+
+def get_dataset_name(config):
+    # If there are more dataset, add here.
+    name_map = dict(
+        CityscapesDataset='Cityscapes',
+        CocoDataset='COCO',
+        CocoPanopticDataset='COCO',
+        DeepFashionDataset='Deep Fashion',
+        LVISV05Dataset='LVIS v0.5',
+        LVISV1Dataset='LVIS v1',
+        VOCDataset='Pascal VOC',
+        WIDERFaceDataset='WIDER Face',
+        OpenImagesDataset='OpenImagesDataset',
+        OpenImagesChallengeDataset='OpenImagesChallengeDataset',
+        Objects365V1Dataset='Objects365 v1',
+        Objects365V2Dataset='Objects365 v2')
+    cfg = Config.fromfile('./configs/' + config)
+    return name_map[cfg.dataset_type]
+
+
+def find_last_dir(model_dir):
+    dst_times = []
+    for time_stamp in os.scandir(model_dir):
+        if osp.isdir(time_stamp):
+            dst_time = time.mktime(
+                time.strptime(time_stamp.name, '%Y%m%d_%H%M%S'))
+            dst_times.append([dst_time, time_stamp.name])
+    return max(dst_times, key=lambda x: x[0])[1]
+
+
+def convert_model_info_to_pwc(model_infos):
+    pwc_files = {}
+    for model in model_infos:
+        cfg_folder_name = osp.split(model['config'])[-2]
+        pwc_model_info = OrderedDict()
+        pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0]
+        pwc_model_info['In Collection'] = 'Please fill in Collection name'
+        pwc_model_info['Config'] = osp.join('configs', model['config'])
+
+        # get metadata
+        meta_data = OrderedDict()
+        if 'epochs' in model:
+            meta_data['Epochs'] = get_real_epoch_or_iter(model['config'])
+        else:
+            meta_data['Iterations'] = get_real_epoch_or_iter(model['config'])
+        pwc_model_info['Metadata'] = meta_data
+
+        # get dataset name
+        dataset_name = get_dataset_name(model['config'])
+
+        # get results
+        results = []
+        # if there are more metrics, add here.
+        if 'bbox_mAP' in model['results']:
+            metric = round(model['results']['bbox_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Object Detection',
+                    Dataset=dataset_name,
+                    Metrics={'box AP': metric}))
+        if 'segm_mAP' in model['results']:
+            metric = round(model['results']['segm_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Instance Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'mask AP': metric}))
+        if 'PQ' in model['results']:
+            metric = round(model['results']['PQ'], 1)
+            results.append(
+                OrderedDict(
+                    Task='Panoptic Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'PQ': metric}))
+        pwc_model_info['Results'] = results
+
+        link_string = 'https://download.openmmlab.com/mmdetection/v3.0/'
+        link_string += '{}/{}'.format(model['config'].rstrip('.py'),
+                                      osp.split(model['model_path'])[-1])
+        pwc_model_info['Weights'] = link_string
+        if cfg_folder_name in pwc_files:
+            pwc_files[cfg_folder_name].append(pwc_model_info)
+        else:
+            pwc_files[cfg_folder_name] = [pwc_model_info]
+    return pwc_files
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        'root',
+        type=str,
+        default='work_dirs',
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='gather',
+        help='output path of gathered models to be stored')
+    parser.add_argument(
+        '--best',
+        action='store_true',
+        help='whether to gather the best model.')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    models_root = args.root
+    models_out = args.out
+    mkdir_or_exist(models_out)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        if osp.exists(osp.join(models_root, raw_config)):
+            used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        exp_dir = osp.join(models_root, used_config)
+        by_epoch = is_by_epoch(used_config)
+        # check whether the exps is finished
+        if args.best is True:
+            final_model, final_epoch_or_iter = get_best_epoch_or_iter(exp_dir)
+        else:
+            final_epoch_or_iter = get_final_epoch_or_iter(used_config)
+            final_model = '{}_{}.pth'.format('epoch' if by_epoch else 'iter',
+                                             final_epoch_or_iter)
+
+        model_path = osp.join(exp_dir, final_model)
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            continue
+
+        # get the latest logs
+        latest_exp_name = find_last_dir(exp_dir)
+        latest_exp_json = osp.join(exp_dir, latest_exp_name, 'vis_data',
+                                   latest_exp_name + '.json')
+
+        model_performance = get_final_results(
+            latest_exp_json, final_epoch_or_iter, by_epoch=by_epoch)
+
+        if model_performance is None:
+            continue
+
+        model_info = dict(
+            config=used_config,
+            results=model_performance,
+            final_model=final_model,
+            latest_exp_json=latest_exp_json,
+            latest_exp_name=latest_exp_name)
+        model_info['epochs' if by_epoch else 'iterations'] =\
+            final_epoch_or_iter
+        model_infos.append(model_info)
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
+        mkdir_or_exist(model_publish_dir)
+
+        model_name = osp.split(model['config'])[-1].split('.')[0]
+
+        model_name += '_' + model['latest_exp_name']
+        publish_model_path = osp.join(model_publish_dir, model_name)
+        trained_model_path = osp.join(models_root, model['config'],
+                                      model['final_model'])
+
+        # convert model
+        final_model_path = process_checkpoint(trained_model_path,
+                                              publish_model_path)
+
+        # copy log
+        shutil.copy(model['latest_exp_json'],
+                    osp.join(model_publish_dir, f'{model_name}.log.json'))
+
+        # copy config to guarantee reproducibility
+        config_path = model['config']
+        config_path = osp.join(
+            'configs',
+            config_path) if 'configs' not in config_path else config_path
+        target_config_path = osp.split(config_path)[-1]
+        shutil.copy(config_path, osp.join(model_publish_dir,
+                                          target_config_path))
+
+        model['model_path'] = final_model_path
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    print(f'Totally gathered {len(publish_model_infos)} models')
+    dump(models, osp.join(models_out, 'model_info.json'))
+
+    pwc_files = convert_model_info_to_pwc(publish_model_infos)
+    for name in pwc_files:
+        with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f:
+            ordered_yaml_dump(pwc_files[name], f, encoding='utf-8')
+
+
+if __name__ == '__main__':
+    main()

mmdetection/.dev_scripts/gather_test_benchmark_metric.py

@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+from mmengine.config import Config
+from mmengine.fileio import dump, load
+from mmengine.utils import mkdir_or_exist
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--show-all', action='store_true', help='show all model metrics')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    root_path = args.root
+    metrics_out = args.out
+    result_dict = {}
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            record_metrics = model_info['metric']
+            config = model_info['config'].strip()
+            fname, _ = osp.splitext(osp.basename(config))
+            metric_json_dir = osp.join(root_path, fname)
+            if osp.exists(metric_json_dir):
+                json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
+                if len(json_list) > 0:
+                    log_json_path = list(sorted(json_list))[-1]
+
+                    metric = load(log_json_path)
+                    if config in metric.get('config', {}):
+
+                        new_metrics = dict()
+                        for record_metric_key in record_metrics:
+                            record_metric_key_bk = record_metric_key
+                            old_metric = record_metrics[record_metric_key]
+                            if record_metric_key == 'AR_1000':
+                                record_metric_key = 'AR@1000'
+                            if record_metric_key not in metric['metric']:
+                                raise KeyError(
+                                    'record_metric_key not exist, please '
+                                    'check your config')
+                            new_metric = round(
+                                metric['metric'][record_metric_key] * 100, 1)
+                            new_metrics[record_metric_key_bk] = new_metric
+
+                        if args.show_all:
+                            result_dict[config] = dict(
+                                before=record_metrics, after=new_metrics)
+                        else:
+                            for record_metric_key in record_metrics:
+                                old_metric = record_metrics[record_metric_key]
+                                new_metric = new_metrics[record_metric_key]
+                                if old_metric != new_metric:
+                                    result_dict[config] = dict(
+                                        before=record_metrics,
+                                        after=new_metrics)
+                                    break
+                    else:
+                        print(f'{config} not included in: {log_json_path}')
+                else:
+                    print(f'{config} not exist file: {metric_json_dir}')
+            else:
+                print(f'{config} not exist dir: {metric_json_dir}')
+
+    if metrics_out:
+        mkdir_or_exist(metrics_out)
+        dump(result_dict, osp.join(metrics_out, 'batch_test_metric_info.json'))
+    if not args.not_show:
+        print('===================================')
+        for config_name, metrics in result_dict.items():
+            print(config_name, metrics)
+        print('===================================')

mmdetection/.dev_scripts/gather_train_benchmark_metric.py

@@ -0,0 +1,151 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+from gather_models import get_final_results
+from mmengine.config import Config
+from mmengine.fileio import dump
+from mmengine.utils import mkdir_or_exist
+
+try:
+    import xlrd
+except ImportError:
+    xlrd = None
+try:
+    import xlutils
+    from xlutils.copy import copy
+except ImportError:
+    xlutils = None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--excel', type=str, help='input path of excel to be recorded')
+    parser.add_argument(
+        '--ncol', type=int, help='Number of column to be modified or appended')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if args.excel:
+        assert args.ncol, 'Please specify "--excel" and "--ncol" ' \
+                          'at the same time'
+        if xlrd is None:
+            raise RuntimeError(
+                'xlrd is not installed,'
+                'Please use “pip install xlrd==1.2.0” to install')
+        if xlutils is None:
+            raise RuntimeError(
+                'xlutils is not installed,'
+                'Please use “pip install xlutils==2.0.0” to install')
+        readbook = xlrd.open_workbook(args.excel)
+        sheet = readbook.sheet_by_name('Sheet1')
+        sheet_info = {}
+        total_nrows = sheet.nrows
+        for i in range(3, sheet.nrows):
+            sheet_info[sheet.row_values(i)[0]] = i
+        xlrw = copy(readbook)
+        table = xlrw.get_sheet(0)
+
+    root_path = args.root
+    metrics_out = args.out
+
+    result_dict = {}
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, config in enumerate(model_cfgs):
+            config = config.strip()
+            if len(config) == 0:
+                continue
+
+            config_name = osp.split(config)[-1]
+            config_name = osp.splitext(config_name)[0]
+            result_path = osp.join(root_path, config_name)
+            if osp.exists(result_path):
+                # 1 read config
+                cfg = Config.fromfile(config)
+                total_epochs = cfg.runner.max_epochs
+                final_results = cfg.evaluation.metric
+                if not isinstance(final_results, list):
+                    final_results = [final_results]
+                final_results_out = []
+                for key in final_results:
+                    if 'proposal_fast' in key:
+                        final_results_out.append('AR@1000')  # RPN
+                    elif 'mAP' not in key:
+                        final_results_out.append(key + '_mAP')
+
+                # 2 determine whether total_epochs ckpt exists
+                ckpt_path = f'epoch_{total_epochs}.pth'
+                if osp.exists(osp.join(result_path, ckpt_path)):
+                    log_json_path = list(
+                        sorted(glob.glob(osp.join(result_path,
+                                                  '*.log.json'))))[-1]
+
+                    # 3 read metric
+                    model_performance = get_final_results(
+                        log_json_path, total_epochs, final_results_out)
+                    if model_performance is None:
+                        print(f'log file error: {log_json_path}')
+                        continue
+                    for performance in model_performance:
+                        if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']:
+                            metric = round(
+                                model_performance[performance] * 100, 1)
+                            model_performance[performance] = metric
+                    result_dict[config] = model_performance
+
+                    # update and append excel content
+                    if args.excel:
+                        if 'AR@1000' in model_performance:
+                            metrics = f'{model_performance["AR@1000"]}' \
+                                      f'(AR@1000)'
+                        elif 'segm_mAP' in model_performance:
+                            metrics = f'{model_performance["bbox_mAP"]}/' \
+                                      f'{model_performance["segm_mAP"]}'
+                        else:
+                            metrics = f'{model_performance["bbox_mAP"]}'
+
+                        row_num = sheet_info.get(config, None)
+                        if row_num:
+                            table.write(row_num, args.ncol, metrics)
+                        else:
+                            table.write(total_nrows, 0, config)
+                            table.write(total_nrows, args.ncol, metrics)
+                            total_nrows += 1
+
+                else:
+                    print(f'{config} not exist: {ckpt_path}')
+            else:
+                print(f'not exist: {config}')
+
+        # 4 save or print results
+        if metrics_out:
+            mkdir_or_exist(metrics_out)
+            dump(result_dict, osp.join(metrics_out, 'model_metric_info.json'))
+        if not args.not_show:
+            print('===================================')
+            for config_name, metrics in result_dict.items():
+                print(config_name, metrics)
+            print('===================================')
+        if args.excel:
+            filename, sufflx = osp.splitext(args.excel)
+            xlrw.save(f'{filename}_o{sufflx}')
+            print(f'>>> Output {filename}_o{sufflx}')

mmdetection/.dev_scripts/linter.sh

@@ -0,0 +1,3 @@
+yapf -r -i mmdet/ configs/ tests/ tools/
+isort -rc mmdet/ configs/ tests/ tools/
+flake8 .

mmdetection/.dev_scripts/test_benchmark.sh

@@ -0,0 +1,157 @@
+PARTITION=$1
+CHECKPOINT_DIR=$2
+WORK_DIR=$3
+CPUS_PER_TASK=${4:-2}
+
+echo 'configs/atss/atss_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py $CHECKPOINT_DIR/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth --work-dir $WORK_DIR/atss_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29666  &
+echo 'configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION autoassign_r50-caffe_fpn_1x_coco configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py $CHECKPOINT_DIR/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth --work-dir $WORK_DIR/autoassign_r50-caffe_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29667  &
+echo 'configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_fpn-carafe_1x_coco configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth --work-dir $WORK_DIR/faster-rcnn_r50_fpn-carafe_1x_coco --cfg-option env_cfg.dist_cfg.port=29668  &
+echo 'configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION cascade-rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth --work-dir $WORK_DIR/cascade-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29669  &
+echo 'configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION cascade-mask-rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth --work-dir $WORK_DIR/cascade-mask-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29670  &
+echo 'configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py $CHECKPOINT_DIR/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth --work-dir $WORK_DIR/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29671  &
+echo 'configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION centernet_r18-dcnv2_8xb16-crop512-140e_coco configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py $CHECKPOINT_DIR/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth --work-dir $WORK_DIR/centernet_r18-dcnv2_8xb16-crop512-140e_coco --cfg-option env_cfg.dist_cfg.port=29672  &
+echo 'configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py $CHECKPOINT_DIR/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth --work-dir $WORK_DIR/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco --cfg-option env_cfg.dist_cfg.port=29673  &
+echo 'configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco configs/convnext/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py $CHECKPOINT_DIR/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth --work-dir $WORK_DIR/cascade-mask-rcnn_convnext-s-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco --cfg-option env_cfg.dist_cfg.port=29674  &
+echo 'configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION cornernet_hourglass104_8xb6-210e-mstest_coco configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py $CHECKPOINT_DIR/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth --work-dir $WORK_DIR/cornernet_hourglass104_8xb6-210e-mstest_coco --cfg-option env_cfg.dist_cfg.port=29675  &
+echo 'configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth --work-dir $WORK_DIR/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29676  &
+echo 'configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_fpn_mdpool_1x_coco configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth --work-dir $WORK_DIR/faster-rcnn_r50_fpn_mdpool_1x_coco --cfg-option env_cfg.dist_cfg.port=29677  &
+echo 'configs/ddod/ddod_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION ddod_r50_fpn_1x_coco configs/ddod/ddod_r50_fpn_1x_coco.py $CHECKPOINT_DIR/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth --work-dir $WORK_DIR/ddod_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29678  &
+echo 'configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION deformable-detr_r50_16xb2-50e_coco configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py $CHECKPOINT_DIR/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth --work-dir $WORK_DIR/deformable-detr_r50_16xb2-50e_coco --cfg-option env_cfg.dist_cfg.port=29679  &
+echo 'configs/detectors/detectors_htc-r50_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION detectors_htc-r50_1x_coco configs/detectors/detectors_htc-r50_1x_coco.py $CHECKPOINT_DIR/detectors_htc_r50_1x_coco-329b1453.pth --work-dir $WORK_DIR/detectors_htc-r50_1x_coco --cfg-option env_cfg.dist_cfg.port=29680  &
+echo 'configs/detr/detr_r50_8xb2-150e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION detr_r50_8xb2-150e_coco configs/detr/detr_r50_8xb2-150e_coco.py $CHECKPOINT_DIR/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth --work-dir $WORK_DIR/detr_r50_8xb2-150e_coco --cfg-option env_cfg.dist_cfg.port=29681  &
+echo 'configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION dh-faster-rcnn_r50_fpn_1x_coco configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth --work-dir $WORK_DIR/dh-faster-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29682  &
+echo 'configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION atss_r50_fpn_dyhead_1x_coco configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py $CHECKPOINT_DIR/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth --work-dir $WORK_DIR/atss_r50_fpn_dyhead_1x_coco --cfg-option env_cfg.dist_cfg.port=29683  &
+echo 'configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION dynamic-rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dynamic_rcnn_r50_fpn_1x-62a3f276.pth --work-dir $WORK_DIR/dynamic-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29684  &
+echo 'configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION retinanet_effb3_fpn_8xb4-crop896-1x_coco configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py $CHECKPOINT_DIR/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth --work-dir $WORK_DIR/retinanet_effb3_fpn_8xb4-crop896-1x_coco --cfg-option env_cfg.dist_cfg.port=29685  &
+echo 'configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50-attn1111_fpn_1x_coco configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth --work-dir $WORK_DIR/faster-rcnn_r50-attn1111_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29686  &
+echo 'configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --work-dir $WORK_DIR/faster-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29687  &
+echo 'configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py $CHECKPOINT_DIR/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth --work-dir $WORK_DIR/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco --cfg-option env_cfg.dist_cfg.port=29688  &
+echo 'configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION fovea_r50_fpn_gn-head-align_4xb4-2x_coco configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py $CHECKPOINT_DIR/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth --work-dir $WORK_DIR/fovea_r50_fpn_gn-head-align_4xb4-2x_coco --cfg-option env_cfg.dist_cfg.port=29689  &
+echo 'configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50_fpg_crop640-50e_coco configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth --work-dir $WORK_DIR/mask-rcnn_r50_fpg_crop640-50e_coco --cfg-option env_cfg.dist_cfg.port=29690  &
+echo 'configs/free_anchor/freeanchor_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION freeanchor_r50_fpn_1x_coco configs/free_anchor/freeanchor_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth --work-dir $WORK_DIR/freeanchor_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29691  &
+echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py $CHECKPOINT_DIR/fsaf_r50_fpn_1x_coco-94ccc51f.pth --work-dir $WORK_DIR/fsaf_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29692  &
+echo 'configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth --work-dir $WORK_DIR/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29693  &
+echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py $CHECKPOINT_DIR/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth --work-dir $WORK_DIR/gfl_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29694  &
+echo 'configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION retinanet_r50_fpn_ghm-1x_coco configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py $CHECKPOINT_DIR/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth --work-dir $WORK_DIR/retinanet_r50_fpn_ghm-1x_coco --cfg-option env_cfg.dist_cfg.port=29695  &
+echo 'configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth --work-dir $WORK_DIR/mask-rcnn_r50_fpn_gn-all_2x_coco --cfg-option env_cfg.dist_cfg.port=29696  &
+echo 'configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_fpn_gn-ws-all_1x_coco configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth --work-dir $WORK_DIR/faster-rcnn_r50_fpn_gn-ws-all_1x_coco --cfg-option env_cfg.dist_cfg.port=29697  &
+echo 'configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION grid-rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py $CHECKPOINT_DIR/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth --work-dir $WORK_DIR/grid-rcnn_r50_fpn_gn-head_2x_coco --cfg-option env_cfg.dist_cfg.port=29698  &
+echo 'configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faste-rcnn_r50_fpn_groie_1x_coco configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth --work-dir $WORK_DIR/faste-rcnn_r50_fpn_groie_1x_coco --cfg-option env_cfg.dist_cfg.port=29699  &
+echo 'configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION ga-retinanet_r50-caffe_fpn_1x_coco configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth --work-dir $WORK_DIR/ga-retinanet_r50-caffe_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29700  &
+echo 'configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_hrnetv2p-w18-1x_coco configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py $CHECKPOINT_DIR/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth --work-dir $WORK_DIR/faster-rcnn_hrnetv2p-w18-1x_coco --cfg-option env_cfg.dist_cfg.port=29701  &
+echo 'configs/htc/htc_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py $CHECKPOINT_DIR/htc_r50_fpn_1x_coco_20200317-7332cf16.pth --work-dir $WORK_DIR/htc_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29702  &
+echo 'configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50_fpn_instaboost-4x_coco configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth --work-dir $WORK_DIR/mask-rcnn_r50_fpn_instaboost-4x_coco --cfg-option env_cfg.dist_cfg.port=29703  &
+echo 'configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION libra-faster-rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth --work-dir $WORK_DIR/libra-faster-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29704  &
+echo 'configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask2former_r50_8xb2-lsj-50e_coco-panoptic configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py $CHECKPOINT_DIR/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth --work-dir $WORK_DIR/mask2former_r50_8xb2-lsj-50e_coco-panoptic --cfg-option env_cfg.dist_cfg.port=29705  &
+echo 'configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50_fpn_1x_coco configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth --work-dir $WORK_DIR/mask-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29706  &
+echo 'configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION maskformer_r50_ms-16xb1-75e_coco configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py $CHECKPOINT_DIR/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth --work-dir $WORK_DIR/maskformer_r50_ms-16xb1-75e_coco --cfg-option env_cfg.dist_cfg.port=29707  &
+echo 'configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION ms-rcnn_r50-caffe_fpn_1x_coco configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth --work-dir $WORK_DIR/ms-rcnn_r50-caffe_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29708  &
+echo 'configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py $CHECKPOINT_DIR/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth --work-dir $WORK_DIR/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco --cfg-option env_cfg.dist_cfg.port=29709  &
+echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION retinanet_r50_nasfpn_crop640-50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py $CHECKPOINT_DIR/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth --work-dir $WORK_DIR/retinanet_r50_nasfpn_crop640-50e_coco --cfg-option env_cfg.dist_cfg.port=29710  &
+echo 'configs/paa/paa_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py $CHECKPOINT_DIR/paa_r50_fpn_1x_coco_20200821-936edec3.pth --work-dir $WORK_DIR/paa_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29711  &
+echo 'configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_pafpn_1x_coco configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth --work-dir $WORK_DIR/faster-rcnn_r50_pafpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29712  &
+echo 'configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION panoptic-fpn_r50_fpn_1x_coco configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth --work-dir $WORK_DIR/panoptic-fpn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29713  &
+echo 'configs/pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_r50_fpn_pisa_1x_coco configs/pisa/faster-rcnn_r50_fpn_pisa_1x_coco.py $CHECKPOINT_DIR/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth --work-dir $WORK_DIR/faster-rcnn_r50_fpn_pisa_1x_coco --cfg-option env_cfg.dist_cfg.port=29714  &
+echo 'configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION point-rend_r50-caffe_fpn_ms-1x_coco configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py $CHECKPOINT_DIR/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth --work-dir $WORK_DIR/point-rend_r50-caffe_fpn_ms-1x_coco --cfg-option env_cfg.dist_cfg.port=29715  &
+echo 'configs/pvt/retinanet_pvt-s_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION retinanet_pvt-s_fpn_1x_coco configs/pvt/retinanet_pvt-s_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth --work-dir $WORK_DIR/retinanet_pvt-s_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29716  &
+echo 'configs/queryinst/queryinst_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION queryinst_r50_fpn_1x_coco configs/queryinst/queryinst_r50_fpn_1x_coco.py $CHECKPOINT_DIR/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth --work-dir $WORK_DIR/queryinst_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29717  &
+echo 'configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth --work-dir $WORK_DIR/mask-rcnn_regnetx-3.2GF_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29718  &
+echo 'configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION reppoints-moment_r50_fpn_1x_coco configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py $CHECKPOINT_DIR/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth --work-dir $WORK_DIR/reppoints-moment_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29719  &
+echo 'configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_res2net-101_fpn_2x_coco configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py $CHECKPOINT_DIR/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth --work-dir $WORK_DIR/faster-rcnn_res2net-101_fpn_2x_coco --cfg-option env_cfg.dist_cfg.port=29720  &
+echo 'configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py $CHECKPOINT_DIR/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth --work-dir $WORK_DIR/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco --cfg-option env_cfg.dist_cfg.port=29721  &
+echo 'configs/resnet_strikes_back/mask-rcnn_r50-rsb-pre_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50-rsb-pre_fpn_1x_coco configs/resnet_strikes_back/mask-rcnn_r50-rsb-pre_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth --work-dir $WORK_DIR/mask-rcnn_r50-rsb-pre_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29722  &
+echo 'configs/retinanet/retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION retinanet_r50_fpn_1x_coco configs/retinanet/retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth --work-dir $WORK_DIR/retinanet_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29723  &
+echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth --work-dir $WORK_DIR/rpn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29724  &
+echo 'configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION sabl-retinanet_r50_fpn_1x_coco configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth --work-dir $WORK_DIR/sabl-retinanet_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29725  &
+echo 'configs/sabl/sabl-faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION sabl-faster-rcnn_r50_fpn_1x_coco configs/sabl/sabl-faster-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth --work-dir $WORK_DIR/sabl-faster-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29726  &
+echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/scnet_r50_fpn_1x_coco-c3f09857.pth --work-dir $WORK_DIR/scnet_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29727  &
+echo 'configs/scratch/mask-rcnn_r50-scratch_fpn_gn-all_6x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_r50-scratch_fpn_gn-all_6x_coco configs/scratch/mask-rcnn_r50-scratch_fpn_gn-all_6x_coco.py $CHECKPOINT_DIR/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth --work-dir $WORK_DIR/mask-rcnn_r50-scratch_fpn_gn-all_6x_coco --cfg-option env_cfg.dist_cfg.port=29728  &
+echo 'configs/solo/decoupled-solo_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION decoupled-solo_r50_fpn_1x_coco configs/solo/decoupled-solo_r50_fpn_1x_coco.py $CHECKPOINT_DIR/decoupled_solo_r50_fpn_1x_coco_20210820_233348-6337c589.pth --work-dir $WORK_DIR/decoupled-solo_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29729  &
+echo 'configs/solov2/solov2_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION solov2_r50_fpn_1x_coco configs/solov2/solov2_r50_fpn_1x_coco.py $CHECKPOINT_DIR/solov2_r50_fpn_1x_coco_20220512_125858-a357fa23.pth --work-dir $WORK_DIR/solov2_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29730  &
+echo 'configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION sparse-rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth --work-dir $WORK_DIR/sparse-rcnn_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29731  &
+echo 'configs/ssd/ssd300_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION ssd300_coco configs/ssd/ssd300_coco.py $CHECKPOINT_DIR/ssd300_coco_20210803_015428-d231a06e.pth --work-dir $WORK_DIR/ssd300_coco --cfg-option env_cfg.dist_cfg.port=29732  &
+echo 'configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION ssdlite_mobilenetv2-scratch_8xb24-600e_coco configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py $CHECKPOINT_DIR/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth --work-dir $WORK_DIR/ssdlite_mobilenetv2-scratch_8xb24-600e_coco --cfg-option env_cfg.dist_cfg.port=29733  &
+echo 'configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION mask-rcnn_swin-t-p4-w7_fpn_1x_coco configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth --work-dir $WORK_DIR/mask-rcnn_swin-t-p4-w7_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29734  &
+echo 'configs/tood/tood_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION tood_r50_fpn_1x_coco configs/tood/tood_r50_fpn_1x_coco.py $CHECKPOINT_DIR/tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth --work-dir $WORK_DIR/tood_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29735  &
+echo 'configs/tridentnet/tridentnet_r50-caffe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION tridentnet_r50-caffe_1x_coco configs/tridentnet/tridentnet_r50-caffe_1x_coco.py $CHECKPOINT_DIR/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth --work-dir $WORK_DIR/tridentnet_r50-caffe_1x_coco --cfg-option env_cfg.dist_cfg.port=29736  &
+echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth --work-dir $WORK_DIR/vfnet_r50_fpn_1x_coco --cfg-option env_cfg.dist_cfg.port=29737  &
+echo 'configs/yolact/yolact_r50_1xb8-55e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION yolact_r50_1xb8-55e_coco configs/yolact/yolact_r50_1xb8-55e_coco.py $CHECKPOINT_DIR/yolact_r50_1x8_coco_20200908-f38d58df.pth --work-dir $WORK_DIR/yolact_r50_1xb8-55e_coco --cfg-option env_cfg.dist_cfg.port=29738  &
+echo 'configs/yolo/yolov3_d53_8xb8-320-273e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION yolov3_d53_8xb8-320-273e_coco configs/yolo/yolov3_d53_8xb8-320-273e_coco.py $CHECKPOINT_DIR/yolov3_d53_320_273e_coco-421362b6.pth --work-dir $WORK_DIR/yolov3_d53_8xb8-320-273e_coco --cfg-option env_cfg.dist_cfg.port=29739  &
+echo 'configs/yolof/yolof_r50-c5_8xb8-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION yolof_r50-c5_8xb8-1x_coco configs/yolof/yolof_r50-c5_8xb8-1x_coco.py $CHECKPOINT_DIR/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth --work-dir $WORK_DIR/yolof_r50-c5_8xb8-1x_coco --cfg-option env_cfg.dist_cfg.port=29740  &
+echo 'configs/yolox/yolox_tiny_8xb8-300e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK tools/slurm_test.sh $PARTITION yolox_tiny_8xb8-300e_coco configs/yolox/yolox_tiny_8xb8-300e_coco.py $CHECKPOINT_DIR/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth --work-dir $WORK_DIR/yolox_tiny_8xb8-300e_coco --cfg-option env_cfg.dist_cfg.port=29741  &

mmdetection/.dev_scripts/test_init_backbone.py

@@ -0,0 +1,178 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Check out backbone whether successfully load pretrained checkpoint."""
+import copy
+import os
+from os.path import dirname, exists, join
+
+import pytest
+from mmengine.config import Config
+from mmengine.runner import CheckpointLoader
+from mmengine.utils import ProgressBar
+
+from mmdet.registry import MODELS
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def _get_detector_cfg(fname):
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
+    """
+    config = _get_config_module(fname)
+    model = copy.deepcopy(config.model)
+    return model
+
+
+def _traversed_config_file():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    If the `backbone.init_cfg` is None (do not use `Pretrained` init way), you
+    need add the folder name in `ignores_folder` (if the config files in this
+    folder all set backbone.init_cfg is None) or add config name in
+    `ignores_file` (if the config file set backbone.init_cfg is None)
+    """
+    config_path = _get_config_directory()
+    check_cfg_names = []
+
+    # `base`, `legacy_1.x` and `common` ignored by default.
+    ignores_folder = ['_base_', 'legacy_1.x', 'common']
+    # 'ld' need load teacher model, if want to check 'ld',
+    # please check teacher_config path first.
+    ignores_folder += ['ld']
+    # `selfsup_pretrain` need convert model, if want to check this model,
+    # need to convert the model first.
+    ignores_folder += ['selfsup_pretrain']
+
+    # the `init_cfg` in 'centripetalnet', 'cornernet', 'cityscapes',
+    # 'scratch' is None.
+    # the `init_cfg` in ssdlite(`ssdlite_mobilenetv2_scratch_600e_coco.py`)
+    # is None
+    # Please confirm `bockbone.init_cfg` is None first.
+    ignores_folder += ['centripetalnet', 'cornernet', 'cityscapes', 'scratch']
+    ignores_file = ['ssdlite_mobilenetv2_scratch_600e_coco.py']
+
+    for config_file_name in os.listdir(config_path):
+        if config_file_name not in ignores_folder:
+            config_file = join(config_path, config_file_name)
+            if os.path.isdir(config_file):
+                for config_sub_file in os.listdir(config_file):
+                    if config_sub_file.endswith('py') and \
+                            config_sub_file not in ignores_file:
+                        name = join(config_file, config_sub_file)
+                        check_cfg_names.append(name)
+    return check_cfg_names
+
+
+def _check_backbone(config, print_cfg=True):
+    """Check out backbone whether successfully load pretrained model, by using
+    `backbone.init_cfg`.
+
+    First, using `CheckpointLoader.load_checkpoint` to load the checkpoint
+        without loading models.
+    Then, using `MODELS.build` to build models, and using
+        `model.init_weights()` to initialize the parameters.
+    Finally, assert weights and bias of each layer loaded from pretrained
+        checkpoint are equal to the weights and bias of original checkpoint.
+        For the convenience of comparison, we sum up weights and bias of
+        each loaded layer separately.
+
+    Args:
+        config (str): Config file path.
+        print_cfg (bool): Whether print logger and return the result.
+
+    Returns:
+        results (str or None): If backbone successfully load pretrained
+            checkpoint, return None; else, return config file path.
+    """
+    if print_cfg:
+        print('-' * 15 + 'loading ', config)
+    cfg = Config.fromfile(config)
+    init_cfg = None
+    try:
+        init_cfg = cfg.model.backbone.init_cfg
+        init_flag = True
+    except AttributeError:
+        init_flag = False
+    if init_cfg is None or init_cfg.get('type') != 'Pretrained':
+        init_flag = False
+    if init_flag:
+        checkpoint = CheckpointLoader.load_checkpoint(init_cfg.checkpoint)
+        if 'state_dict' in checkpoint:
+            state_dict = checkpoint['state_dict']
+        else:
+            state_dict = checkpoint
+
+        model = MODELS.build(cfg.model)
+        model.init_weights()
+
+        checkpoint_layers = state_dict.keys()
+        for name, value in model.backbone.state_dict().items():
+            if name in checkpoint_layers:
+                assert value.equal(state_dict[name])
+
+        if print_cfg:
+            print('-' * 10 + 'Successfully load checkpoint' + '-' * 10 +
+                  '\n', )
+            return None
+    else:
+        if print_cfg:
+            print(config + '\n' + '-' * 10 +
+                  'config file do not have init_cfg' + '-' * 10 + '\n')
+            return config
+
+
+@pytest.mark.parametrize('config', _traversed_config_file())
+def test_load_pretrained(config):
+    """Check out backbone whether successfully load pretrained model by using
+    `backbone.init_cfg`.
+
+    Details please refer to `_check_backbone`
+    """
+    _check_backbone(config, print_cfg=False)
+
+
+def _test_load_pretrained():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    Returns:
+        check_cfg_names (list[str]): Config files that backbone initialized
+        from pretrained checkpoint might be problematic. Need to recheck
+        the config file. The output including the config files that the
+        backbone.init_cfg is None
+    """
+    check_cfg_names = _traversed_config_file()
+    need_check_cfg = []
+
+    prog_bar = ProgressBar(len(check_cfg_names))
+    for config in check_cfg_names:
+        init_cfg_name = _check_backbone(config)
+        if init_cfg_name is not None:
+            need_check_cfg.append(init_cfg_name)
+        prog_bar.update()
+    print('These config files need to be checked again')
+    print(need_check_cfg)

mmdetection/.dev_scripts/train_benchmark.sh

@@ -0,0 +1,164 @@
+PARTITION=$1
+WORK_DIR=$2
+CPUS_PER_TASK=${3:-4}
+
+echo 'configs/albu_example/mask-rcnn_r50_fpn_albu_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpn_albu_1x_coco configs/albu_example/mask-rcnn_r50_fpn_albu_1x_coco.py $WORK_DIR/mask-rcnn_r50_fpn_albu_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/atss/atss_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py $WORK_DIR/atss_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION autoassign_r50-caffe_fpn_1x_coco configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py $WORK_DIR/autoassign_r50-caffe_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50_fpn-carafe_1x_coco configs/carafe/faster-rcnn_r50_fpn-carafe_1x_coco.py $WORK_DIR/faster-rcnn_r50_fpn-carafe_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION cascade-rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py $WORK_DIR/cascade-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION cascade-mask-rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py $WORK_DIR/cascade-mask-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py $WORK_DIR/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION centernet_r18-dcnv2_8xb16-crop512-140e_coco configs/centernet/centernet_r18-dcnv2_8xb16-crop512-140e_coco.py $WORK_DIR/centernet_r18-dcnv2_8xb16-crop512-140e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION centernet-update_r50-caffe_fpn_ms-1x_coco configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py $WORK_DIR/centernet-update_r50-caffe_fpn_ms-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py' &
+GPUS=16  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco configs/centripetalnet/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco.py $WORK_DIR/centripetalnet_hourglass104_16xb6-crop511-210e-mstest_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION cornernet_hourglass104_8xb6-210e-mstest_coco configs/cornernet/cornernet_hourglass104_8xb6-210e-mstest_coco.py $WORK_DIR/cornernet_hourglass104_8xb6-210e-mstest_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco configs/convnext/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco.py $WORK_DIR/mask-rcnn_convnext-t-p4-w7_fpn_amp-ms-crop-3x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco configs/dcn/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco.py $WORK_DIR/faster-rcnn_r50-dconv-c3-c5_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50_fpn_mdpool_1x_coco configs/dcnv2/faster-rcnn_r50_fpn_mdpool_1x_coco.py $WORK_DIR/faster-rcnn_r50_fpn_mdpool_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ddod/ddod_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ddod_r50_fpn_1x_coco configs/ddod/ddod_r50_fpn_1x_coco.py $WORK_DIR/ddod_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/detectors/detectors_htc-r50_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION detectors_htc-r50_1x_coco configs/detectors/detectors_htc-r50_1x_coco.py $WORK_DIR/detectors_htc-r50_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py' &
+GPUS=16  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION deformable-detr_r50_16xb2-50e_coco configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py $WORK_DIR/deformable-detr_r50_16xb2-50e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/detr/detr_r50_8xb2-150e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION detr_r50_8xb2-150e_coco configs/detr/detr_r50_8xb2-150e_coco.py $WORK_DIR/detr_r50_8xb2-150e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION dh-faster-rcnn_r50_fpn_1x_coco configs/double_heads/dh-faster-rcnn_r50_fpn_1x_coco.py $WORK_DIR/dh-faster-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION dynamic-rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic-rcnn_r50_fpn_1x_coco.py $WORK_DIR/dynamic-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION atss_r50_fpn_dyhead_1x_coco configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py $WORK_DIR/atss_r50_fpn_dyhead_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_effb3_fpn_8xb4-crop896-1x_coco configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py $WORK_DIR/retinanet_effb3_fpn_8xb4-crop896-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50-attn1111_fpn_1x_coco configs/empirical_attention/faster-rcnn_r50-attn1111_fpn_1x_coco.py $WORK_DIR/faster-rcnn_r50-attn1111_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py $WORK_DIR/faster-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50-caffe-dc5_ms-1x_coco configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py $WORK_DIR/faster-rcnn_r50-caffe-dc5_ms-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco configs/fcos/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco.py $WORK_DIR/fcos_r50-caffe_fpn_gn-head-center-normbbox-centeronreg-giou_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION fovea_r50_fpn_gn-head-align_4xb4-2x_coco configs/foveabox/fovea_r50_fpn_gn-head-align_4xb4-2x_coco.py $WORK_DIR/fovea_r50_fpn_gn-head-align_4xb4-2x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpg_crop640-50e_coco configs/fpg/mask-rcnn_r50_fpg_crop640-50e_coco.py $WORK_DIR/mask-rcnn_r50_fpg_crop640-50e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/free_anchor/freeanchor_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION freeanchor_r50_fpn_1x_coco configs/free_anchor/freeanchor_r50_fpn_1x_coco.py $WORK_DIR/freeanchor_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py $WORK_DIR/fsaf_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco configs/gcnet/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco.py $WORK_DIR/mask-rcnn_r50-syncbn-gcb-r16-c3-c5_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py $WORK_DIR/gfl_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_r50_fpn_ghm-1x_coco configs/ghm/retinanet_r50_fpn_ghm-1x_coco.py $WORK_DIR/retinanet_r50_fpn_ghm-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask-rcnn_r50_fpn_gn-all_2x_coco.py $WORK_DIR/mask-rcnn_r50_fpn_gn-all_2x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50_fpn_gn-ws-all_1x_coco configs/gn+ws/faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py $WORK_DIR/faster-rcnn_r50_fpn_gn-ws-all_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION grid-rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid-rcnn_r50_fpn_gn-head_2x_coco.py $WORK_DIR/grid-rcnn_r50_fpn_gn-head_2x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faste-rcnn_r50_fpn_groie_1x_coco configs/groie/faste-rcnn_r50_fpn_groie_1x_coco.py $WORK_DIR/faste-rcnn_r50_fpn_groie_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ga-retinanet_r50-caffe_fpn_1x_coco configs/guided_anchoring/ga-retinanet_r50-caffe_fpn_1x_coco.py $WORK_DIR/ga-retinanet_r50-caffe_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_hrnetv2p-w18-1x_coco configs/hrnet/faster-rcnn_hrnetv2p-w18-1x_coco.py $WORK_DIR/faster-rcnn_hrnetv2p-w18-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/htc/htc_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py $WORK_DIR/htc_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpn_instaboost-4x_coco configs/instaboost/mask-rcnn_r50_fpn_instaboost-4x_coco.py $WORK_DIR/mask-rcnn_r50_fpn_instaboost-4x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py' &
+GPUS=2  GPUS_PER_NODE=2  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION lad_r50-paa-r101_fpn_2xb8_coco_1x configs/lad/lad_r50-paa-r101_fpn_2xb8_coco_1x.py $WORK_DIR/lad_r50-paa-r101_fpn_2xb8_coco_1x --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ld/ld_r18-gflv1-r101_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ld_r18-gflv1-r101_fpn_1x_coco configs/ld/ld_r18-gflv1-r101_fpn_1x_coco.py $WORK_DIR/ld_r18-gflv1-r101_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION libra-faster-rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py $WORK_DIR/libra-faster-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask2former_r50_8xb2-lsj-50e_coco-panoptic configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py $WORK_DIR/mask2former_r50_8xb2-lsj-50e_coco-panoptic --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpn_1x_coco configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py $WORK_DIR/mask-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py' &
+GPUS=16  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION maskformer_r50_ms-16xb1-75e_coco configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py $WORK_DIR/maskformer_r50_ms-16xb1-75e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ms-rcnn_r50-caffe_fpn_1x_coco configs/ms_rcnn/ms-rcnn_r50-caffe_fpn_1x_coco.py $WORK_DIR/ms-rcnn_r50-caffe_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco configs/nas_fcos/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco.py $WORK_DIR/nas-fcos_r50-caffe_fpn_nashead-gn-head_4xb4-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_r50_nasfpn_crop640-50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640-50e_coco.py $WORK_DIR/retinanet_r50_nasfpn_crop640-50e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/paa/paa_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py $WORK_DIR/paa_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50_pafpn_1x_coco configs/pafpn/faster-rcnn_r50_pafpn_1x_coco.py $WORK_DIR/faster-rcnn_r50_pafpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION panoptic-fpn_r50_fpn_1x_coco configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py $WORK_DIR/panoptic-fpn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/pisa/mask-rcnn_r50_fpn_pisa_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_r50_fpn_pisa_1x_coco configs/pisa/mask-rcnn_r50_fpn_pisa_1x_coco.py $WORK_DIR/mask-rcnn_r50_fpn_pisa_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION point-rend_r50-caffe_fpn_ms-1x_coco configs/point_rend/point-rend_r50-caffe_fpn_ms-1x_coco.py $WORK_DIR/point-rend_r50-caffe_fpn_ms-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/pvt/retinanet_pvt-t_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_pvt-t_fpn_1x_coco configs/pvt/retinanet_pvt-t_fpn_1x_coco.py $WORK_DIR/retinanet_pvt-t_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/queryinst/queryinst_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION queryinst_r50_fpn_1x_coco configs/queryinst/queryinst_r50_fpn_1x_coco.py $WORK_DIR/queryinst_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_regnetx-800MF_fpn_1x_coco configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py $WORK_DIR/retinanet_regnetx-800MF_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION reppoints-moment_r50_fpn_1x_coco configs/reppoints/reppoints-moment_r50_fpn_1x_coco.py $WORK_DIR/reppoints-moment_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_res2net-101_fpn_2x_coco configs/res2net/faster-rcnn_res2net-101_fpn_2x_coco.py $WORK_DIR/faster-rcnn_res2net-101_fpn_2x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco configs/resnest/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py $WORK_DIR/faster-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/resnet_strikes_back/retinanet_r50-rsb-pre_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_r50-rsb-pre_fpn_1x_coco configs/resnet_strikes_back/retinanet_r50-rsb-pre_fpn_1x_coco.py $WORK_DIR/retinanet_r50-rsb-pre_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION retinanet_r50-caffe_fpn_1x_coco configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py $WORK_DIR/retinanet_r50-caffe_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py $WORK_DIR/rpn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION sabl-retinanet_r50_fpn_1x_coco configs/sabl/sabl-retinanet_r50_fpn_1x_coco.py $WORK_DIR/sabl-retinanet_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py $WORK_DIR/scnet_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/scratch/faster-rcnn_r50-scratch_fpn_gn-all_6x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION faster-rcnn_r50-scratch_fpn_gn-all_6x_coco configs/scratch/faster-rcnn_r50-scratch_fpn_gn-all_6x_coco.py $WORK_DIR/faster-rcnn_r50-scratch_fpn_gn-all_6x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/solo/solo_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION solo_r50_fpn_1x_coco configs/solo/solo_r50_fpn_1x_coco.py $WORK_DIR/solo_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/solov2/solov2_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION solov2_r50_fpn_1x_coco configs/solov2/solov2_r50_fpn_1x_coco.py $WORK_DIR/solov2_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION sparse-rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse-rcnn_r50_fpn_1x_coco.py $WORK_DIR/sparse-rcnn_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ssd/ssd300_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ssd300_coco configs/ssd/ssd300_coco.py $WORK_DIR/ssd300_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION ssdlite_mobilenetv2-scratch_8xb24-600e_coco configs/ssd/ssdlite_mobilenetv2-scratch_8xb24-600e_coco.py $WORK_DIR/ssdlite_mobilenetv2-scratch_8xb24-600e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION mask-rcnn_swin-t-p4-w7_fpn_1x_coco configs/swin/mask-rcnn_swin-t-p4-w7_fpn_1x_coco.py $WORK_DIR/mask-rcnn_swin-t-p4-w7_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/tood/tood_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION tood_r50_fpn_1x_coco configs/tood/tood_r50_fpn_1x_coco.py $WORK_DIR/tood_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo ''configs/tridentnet/tridentnet_r50-caffe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION tridentnet_r50-caffe_1x_coco 'configs/tridentnet/tridentnet_r50-caffe_1x_coco.py $WORK_DIR/tridentnet_r50-caffe_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py $WORK_DIR/vfnet_r50_fpn_1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/yolact/yolact_r50_8xb8-55e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION yolact_r50_8xb8-55e_coco configs/yolact/yolact_r50_8xb8-55e_coco.py $WORK_DIR/yolact_r50_8xb8-55e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/yolo/yolov3_d53_8xb8-320-273e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION yolov3_d53_8xb8-320-273e_coco configs/yolo/yolov3_d53_8xb8-320-273e_coco.py $WORK_DIR/yolov3_d53_8xb8-320-273e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/yolof/yolof_r50-c5_8xb8-1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION yolof_r50-c5_8xb8-1x_coco configs/yolof/yolof_r50-c5_8xb8-1x_coco.py $WORK_DIR/yolof_r50-c5_8xb8-1x_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &
+echo 'configs/yolox/yolox_tiny_8xb8-300e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=$CPUS_PRE_TASK ./tools/slurm_train.sh $PARTITION yolox_tiny_8xb8-300e_coco configs/yolox/yolox_tiny_8xb8-300e_coco.py $WORK_DIR/yolox_tiny_8xb8-300e_coco --cfg-options default_hooks.checkpoint.max_keep_ckpts=1 &

mmdetection/.gitignore

@@ -0,0 +1,123 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+data/
+data
+.vscode
+.idea
+.DS_Store
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+docs/modelzoo_statistics.md
+mmdet/.mim
+work_dirs/
+
+# Pytorch
+*.pth
+*.py~
+*.sh~

mmdetection/.owners.yml

@@ -0,0 +1,14 @@
+assign:
+  strategy:
+    # random
+    daily-shift-based
+  scedule:
+    '*/1 * * * *'
+  assignees:
+    - Czm369
+    - hhaAndroid
+    - jbwang1997
+    - RangiLyu
+    - BIGWangYuDong
+    - chhluo
+    - ZwwWayne

mmdetection/.pre-commit-config-zh-cn.yaml

@@ -0,0 +1,61 @@
+exclude: ^tests/data/
+repos:
+  - repo: https://gitee.com/openmmlab/mirrors-flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://gitee.com/openmmlab/mirrors-isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://gitee.com/openmmlab/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://gitee.com/openmmlab/mirrors-mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://gitee.com/openmmlab/mirrors-codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://gitee.com/openmmlab/mirrors-docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://gitee.com/openmmlab/mirrors-pyupgrade
+    rev: v3.0.0
+    hooks:
+      - id: pyupgrade
+        args: ["--py36-plus"]
+  - repo: https://gitee.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet"]
+#  - repo: https://gitee.com/openmmlab/mirrors-mypy
+#    rev: v0.812
+#    hooks:
+#      - id: mypy
+#        exclude: "docs"

mmdetection/.pre-commit-config.yaml

@@ -0,0 +1,50 @@
+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0  # Use the ref you want to point at
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet"]  # replace the dir_to_check with your expected directory to check

mmdetection/.readthedocs.yml

@@ -0,0 +1,14 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt

mmdetection/CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "MMDetection Contributors"
+title: "OpenMMLab Detection Toolbox and Benchmark"
+date-released: 2018-08-22
+url: "https://github.com/open-mmlab/mmdetection"
+license: Apache-2.0

mmdetection/LICENSE

@@ -0,0 +1,203 @@
+Copyright 2018-2023 OpenMMLab. All rights reserved.
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mmdetection/MANIFEST.in

@@ -0,0 +1,7 @@
+include requirements/*.txt
+include mmdet/VERSION
+include mmdet/.mim/model-index.yml
+include mmdet/.mim/dataset-index.yml
+include mmdet/.mim/demo/*/*
+recursive-include mmdet/.mim/configs *.py *.yml
+recursive-include mmdet/.mim/tools *.sh *.py

mmdetection/README.md

@@ -0,0 +1,442 @@
+<div align="center">
+  <img src="resources/mmdet-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab website</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab platform</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)
+[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
+[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+
+[📘Documentation](https://mmdetection.readthedocs.io/en/latest/) |
+[🛠️Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) |
+[👀Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) |
+[🆕Update News](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) |
+[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
+[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+English | [简体中文](README_zh-CN.md)
+
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+<div align="center">
+<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/6c29886f-ae7a-4a55-8be4-352ee85b7d3e"/>
+</div>
+
+## Introduction
+
+MMDetection is an open source object detection toolbox based on PyTorch. It is
+a part of the [OpenMMLab](https://openmmlab.com/) project.
+
+The main branch works with **PyTorch 1.8+**.
+
+<img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
+
+<details open>
+<summary>Major features</summary>
+
+- **Modular Design**
+
+  We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.
+
+- **Support of multiple tasks out of box**
+
+  The toolbox directly supports multiple detection tasks such as **object detection**, **instance segmentation**, **panoptic segmentation**, and **semi-supervised object detection**.
+
+- **High efficiency**
+
+  All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet).
+
+- **State of the art**
+
+  The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward.
+  The newly released [RTMDet](configs/rtmdet) also obtains new state-of-the-art results on real-time instance segmentation and rotated object detection tasks and the best parameter-accuracy trade-off on object detection.
+
+</details>
+
+Apart from MMDetection, we also released [MMEngine](https://github.com/open-mmlab/mmengine) for model training and [MMCV](https://github.com/open-mmlab/mmcv) for computer vision research, which are heavily depended on by this toolbox.
+
+## What's New
+
+### Highlight
+
+We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](configs/rtmdet).
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+**v3.1.0** was released in 30/6/2023:
+
+- Supports tracking algorithms including multi-object tracking (MOT) algorithms SORT, DeepSORT, StrongSORT, OCSORT, ByteTrack, QDTrack, and video instance segmentation (VIS) algorithm MaskTrackRCNN, Mask2Former-VIS.
+- Support [ViTDet](projects/ViTDet)
+- Supports inference and evaluation of multimodal algorithms [GLIP](configs/glip) and [XDecoder](projects/XDecoder), and also supports datasets such as COCO semantic segmentation, COCO Caption, ADE20k general segmentation, and RefCOCO. GLIP fine-tuning will be supported in the future.
+- Provides a [gradio demo](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md) for image type tasks of MMDetection, making it easy for users to experience.
+
+## Installation
+
+Please refer to [Installation](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation instructions.
+
+## Getting Started
+
+Please see [Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the general introduction of MMDetection.
+
+For detailed user guides and advanced guides, please refer to our [documentation](https://mmdetection.readthedocs.io/en/latest/):
+
+- User Guides
+
+  <details>
+
+  - [Train & Test](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#train-test)
+    - [Learn about Configs](https://mmdetection.readthedocs.io/en/latest/user_guides/config.html)
+    - [Inference with existing models](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html)
+    - [Dataset Prepare](https://mmdetection.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
+    - [Test existing models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/test.html)
+    - [Train predefined models on standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html)
+    - [Train with customized datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/train.html#train-with-customized-datasets)
+    - [Train with customized models and standard datasets](https://mmdetection.readthedocs.io/en/latest/user_guides/new_model.html)
+    - [Finetuning Models](https://mmdetection.readthedocs.io/en/latest/user_guides/finetune.html)
+    - [Test Results Submission](https://mmdetection.readthedocs.io/en/latest/user_guides/test_results_submission.html)
+    - [Weight initialization](https://mmdetection.readthedocs.io/en/latest/user_guides/init_cfg.html)
+    - [Use a single stage detector as RPN](https://mmdetection.readthedocs.io/en/latest/user_guides/single_stage_as_rpn.html)
+    - [Semi-supervised Object Detection](https://mmdetection.readthedocs.io/en/latest/user_guides/semi_det.html)
+  - [Useful Tools](https://mmdetection.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
+
+  </details>
+
+- Advanced Guides
+
+  <details>
+
+  - [Basic Concepts](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#basic-concepts)
+  - [Component Customization](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#component-customization)
+  - [How to](https://mmdetection.readthedocs.io/en/latest/advanced_guides/index.html#how-to)
+
+  </details>
+
+We also provide object detection colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) and instance segmentation colab tutorial [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_InstanceSeg_Tutorial.ipynb).
+
+To migrate from MMDetection 2.x, please refer to [migration](https://mmdetection.readthedocs.io/en/latest/migration.html).
+
+## Overview of Benchmark and Model Zoo
+
+Results and models are available in the [model zoo](docs/en/model_zoo.md).
+
+<div align="center">
+  <b>Architectures</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Object Detection</b>
+      </td>
+      <td>
+        <b>Instance Segmentation</b>
+      </td>
+      <td>
+        <b>Panoptic Segmentation</b>
+      </td>
+      <td>
+        <b>Other</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+        <ul>
+            <li><a href="configs/fast_rcnn">Fast R-CNN (ICCV'2015)</a></li>
+            <li><a href="configs/faster_rcnn">Faster R-CNN (NeurIPS'2015)</a></li>
+            <li><a href="configs/rpn">RPN (NeurIPS'2015)</a></li>
+            <li><a href="configs/ssd">SSD (ECCV'2016)</a></li>
+            <li><a href="configs/retinanet">RetinaNet (ICCV'2017)</a></li>
+            <li><a href="configs/cascade_rcnn">Cascade R-CNN (CVPR'2018)</a></li>
+            <li><a href="configs/yolo">YOLOv3 (ArXiv'2018)</a></li>
+            <li><a href="configs/cornernet">CornerNet (ECCV'2018)</a></li>
+            <li><a href="configs/grid_rcnn">Grid R-CNN (CVPR'2019)</a></li>
+            <li><a href="configs/guided_anchoring">Guided Anchoring (CVPR'2019)</a></li>
+            <li><a href="configs/fsaf">FSAF (CVPR'2019)</a></li>
+            <li><a href="configs/centernet">CenterNet (CVPR'2019)</a></li>
+            <li><a href="configs/libra_rcnn">Libra R-CNN (CVPR'2019)</a></li>
+            <li><a href="configs/tridentnet">TridentNet (ICCV'2019)</a></li>
+            <li><a href="configs/fcos">FCOS (ICCV'2019)</a></li>
+            <li><a href="configs/reppoints">RepPoints (ICCV'2019)</a></li>
+            <li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
+            <li><a href="configs/cascade_rpn">CascadeRPN (NeurIPS'2019)</a></li>
+            <li><a href="configs/foveabox">Foveabox (TIP'2020)</a></li>
+            <li><a href="configs/double_heads">Double-Head R-CNN (CVPR'2020)</a></li>
+            <li><a href="configs/atss">ATSS (CVPR'2020)</a></li>
+            <li><a href="configs/nas_fcos">NAS-FCOS (CVPR'2020)</a></li>
+            <li><a href="configs/centripetalnet">CentripetalNet (CVPR'2020)</a></li>
+            <li><a href="configs/autoassign">AutoAssign (ArXiv'2020)</a></li>
+            <li><a href="configs/sabl">Side-Aware Boundary Localization (ECCV'2020)</a></li>
+            <li><a href="configs/dynamic_rcnn">Dynamic R-CNN (ECCV'2020)</a></li>
+            <li><a href="configs/detr">DETR (ECCV'2020)</a></li>
+            <li><a href="configs/paa">PAA (ECCV'2020)</a></li>
+            <li><a href="configs/vfnet">VarifocalNet (CVPR'2021)</a></li>
+            <li><a href="configs/sparse_rcnn">Sparse R-CNN (CVPR'2021)</a></li>
+            <li><a href="configs/yolof">YOLOF (CVPR'2021)</a></li>
+            <li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
+            <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
+            <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
+            <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
+            <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+            <li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
+            <li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
+            <li><a href="configs/dino">DINO (ICLR'2023)</a></li>
+            <li><a href="configs/glip">GLIP (CVPR'2022)</a></li>
+            <li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
+            <li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
+            <li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li>
+          <li><a href="configs/cascade_rcnn">Cascade Mask R-CNN (CVPR'2018)</a></li>
+          <li><a href="configs/ms_rcnn">Mask Scoring R-CNN (CVPR'2019)</a></li>
+          <li><a href="configs/htc">Hybrid Task Cascade (CVPR'2019)</a></li>
+          <li><a href="configs/yolact">YOLACT (ICCV'2019)</a></li>
+          <li><a href="configs/instaboost">InstaBoost (ICCV'2019)</a></li>
+          <li><a href="configs/solo">SOLO (ECCV'2020)</a></li>
+          <li><a href="configs/point_rend">PointRend (CVPR'2020)</a></li>
+          <li><a href="configs/detectors">DetectoRS (ArXiv'2020)</a></li>
+          <li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
+          <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
+          <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
+          <li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
+          <li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
+          <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+          <li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
+          <li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        </ul>
+          <li><b>Contrastive Learning</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/selfsup_pretrain">SwAV (NeurIPS'2020)</a></li>
+          <li><a href="configs/selfsup_pretrain">MoCo (CVPR'2020)</a></li>
+          <li><a href="configs/selfsup_pretrain">MoCov2 (ArXiv'2020)</a></li>
+        </ul>
+        </ul>
+        </ul>
+          <li><b>Distillation</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/ld">Localization Distillation (CVPR'2022)</a></li>
+          <li><a href="configs/lad">Label Assignment Distillation (WACV'2022)</a></li>
+        </ul>
+        </ul>
+          <li><b>Semi-Supervised Object Detection</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/soft_teacher">Soft Teacher (ICCV'2021)</a></li>
+        </ul>
+        </ul>
+      </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+<div align="center">
+  <b>Components</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Backbones</b>
+      </td>
+      <td>
+        <b>Necks</b>
+      </td>
+      <td>
+        <b>Loss</b>
+      </td>
+      <td>
+        <b>Common</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li>VGG (ICLR'2015)</li>
+        <li>ResNet (CVPR'2016)</li>
+        <li>ResNeXt (CVPR'2017)</li>
+        <li>MobileNetV2 (CVPR'2018)</li>
+        <li><a href="configs/hrnet">HRNet (CVPR'2019)</a></li>
+        <li><a href="configs/empirical_attention">Generalized Attention (ICCV'2019)</a></li>
+        <li><a href="configs/gcnet">GCNet (ICCVW'2019)</a></li>
+        <li><a href="configs/res2net">Res2Net (TPAMI'2020)</a></li>
+        <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
+        <li><a href="configs/resnest">ResNeSt (ArXiv'2020)</a></li>
+        <li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
+        <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
+        <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
+        <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
+        <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
+        <li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
+        <li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="configs/pafpn">PAFPN (CVPR'2018)</a></li>
+        <li><a href="configs/nas_fpn">NAS-FPN (CVPR'2019)</a></li>
+        <li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
+        <li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
+        <li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
+        <li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/ghm">GHM (AAAI'2019)</a></li>
+          <li><a href="configs/gfl">Generalized Focal Loss (NeurIPS'2020)</a></li>
+          <li><a href="configs/seesaw_loss">Seasaw Loss (CVPR'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/faster_rcnn/faster-rcnn_r50_fpn_ohem_1x_coco.py">OHEM (CVPR'2016)</a></li>
+          <li><a href="configs/gn">Group Normalization (ECCV'2018)</a></li>
+          <li><a href="configs/dcn">DCN (ICCV'2017)</a></li>
+          <li><a href="configs/dcnv2">DCNv2 (CVPR'2019)</a></li>
+          <li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
+          <li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
+          <li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
+          <li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
+        </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+Some other methods are also supported in [projects using MMDetection](./docs/en/notes/projects.md).
+
+## FAQ
+
+Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
+
+## Contributing
+
+We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmdetection/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
+
+## Acknowledgement
+
+MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
+We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.
+
+## Citation
+
+If you use this toolbox or benchmark in your research, please cite this project.
+
+```
+@article{mmdetection,
+  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
+  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
+             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
+             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
+             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
+             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
+             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
+  journal= {arXiv preprint arXiv:1906.07155},
+  year={2019}
+}
+```
+
+## License
+
+This project is released under the [Apache 2.0 license](LICENSE).
+
+## Projects in OpenMMLab
+
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
+- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
+- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
+- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
+- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.

mmdetection/README_zh-CN.md

@@ -0,0 +1,461 @@
+<div align="center">
+  <img src="resources/mmdet-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/)
+[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions)
+[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection)
+[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues)
+
+[📘使用文档](https://mmdetection.readthedocs.io/zh_CN/latest/) |
+[🛠️安装教程](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) |
+[👀模型库](https://mmdetection.readthedocs.io/zh_CN/latest/model_zoo.html) |
+[🆕更新日志](https://mmdetection.readthedocs.io/en/latest/notes/changelog.html) |
+[🚀进行中的项目](https://github.com/open-mmlab/mmdetection/projects) |
+[🤔报告问题](https://github.com/open-mmlab/mmdetection/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+[English](README.md) | 简体中文
+
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+<div align="center">
+<img src="https://github.com/open-mmlab/mmdetection/assets/17425982/6c29886f-ae7a-4a55-8be4-352ee85b7d3e"/>
+</div>
+
+## 简介
+
+MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
+
+主分支代码目前支持 PyTorch 1.8 及其以上的版本。
+
+<img src="https://user-images.githubusercontent.com/12907710/187674113-2074d658-f2fb-42d1-ac15-9c4a695e64d7.png"/>
+
+<details open>
+<summary>主要特性</summary>
+
+- **模块化设计**
+
+  MMDetection 将检测框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的检测模型
+
+- **支持多种检测任务**
+
+  MMDetection 支持了各种不同的检测任务，包括**目标检测**，**实例分割**，**全景分割**，以及**半监督目标检测**。
+
+- **速度快**
+
+  基本的框和 mask 操作都实现了 GPU 版本，训练速度比其他代码库更快或者相当，包括 [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 和 [SimpleDet](https://github.com/TuSimple/simpledet)。
+
+- **性能高**
+
+  MMDetection 这个算法库源自于 COCO 2018 目标检测竞赛的冠军团队 *MMDet* 团队开发的代码，我们在之后持续进行了改进和提升。
+  新发布的 [RTMDet](configs/rtmdet) 还在实时实例分割和旋转目标检测任务中取得了最先进的成果，同时也在目标检测模型中取得了最佳的的参数量和精度平衡。
+
+</details>
+
+除了 MMDetection 之外，我们还开源了深度学习训练库 [MMEngine](https://github.com/open-mmlab/mmengine) 和计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv)，它们是 MMDetection 的主要依赖。
+
+## 最新进展
+
+### 亮点
+
+我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](configs/rtmdet)找到。
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+**v3.1.0** 版本已经在 2023.6.30 发布：
+
+- 支持 Tracking 类算法，包括多目标跟踪 MOT 算法 SORT、DeepSORT、StrongSORT、OCSORT、ByteTrack、QDTrack 和视频实例分割 VIS 算法 MaskTrackRCNN、Mask2Former-VIS。
+- 支持 [ViTDet](projects/ViTDet)
+- 支持多模态开放检测算法 [GLIP](configs/glip) 和 [XDecoder](projects/XDecoder) 推理和评估，并同时支持了 COCO 语义分割、COCO Caption、ADE20k 通用分割、RefCOCO 等数据集。后续将支持 GLIP 微调
+- 提供了包括 MMDetection 图片任务的 [gradio demo](https://github.com/open-mmlab/mmdetection/blob/dev-3.x/projects/gradio_demo/README.md)，方便用户快速体验
+
+## 安装
+
+请参考[快速入门文档](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html)进行安装。
+
+## 教程
+
+请阅读[概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html)对 MMDetection 进行初步的了解。
+
+为了帮助用户更进一步了解 MMDetection，我们准备了用户指南和进阶指南，请阅读我们的[文档](https://mmdetection.readthedocs.io/zh_CN/latest/)：
+
+- 用户指南
+
+  <details>
+
+  - [训练 & 测试](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/index.html#train-test)
+    - [学习配置文件](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/config.html)
+    - [使用已有模型在标准数据集上进行推理](https://mmdetection.readthedocs.io/en/latest/user_guides/inference.html)
+    - [数据集准备](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
+    - [测试现有模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/test.html)
+    - [在标准数据集上训练预定义的模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/train.html)
+    - [在自定义数据集上进行训练](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/train.html#train-with-customized-datasets)
+    - [在标准数据集上训练自定义模型](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/new_model.html)
+    - [模型微调](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/finetune.html)
+    - [提交测试结果](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/test_results_submission.html)
+    - [权重初始化](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/init_cfg.html)
+    - [将单阶段检测器作为 RPN](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/single_stage_as_rpn.html)
+    - [半监督目标检测](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/semi_det.html)
+  - [实用工具](https://mmdetection.readthedocs.io/zh_CN/latest/user_guides/index.html#useful-tools)
+
+  </details>
+
+- 进阶指南
+
+  <details>
+
+  - [基础概念](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#basic-concepts)
+  - [组件定制](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#component-customization)
+  - [How to](https://mmdetection.readthedocs.io/zh_CN/latest/advanced_guides/index.html#how-to)
+
+  </details>
+
+我们提供了检测的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb) 和 实例分割的 colab 教程 [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](demo/MMDet_Tutorial.ipynb)
+
+同时，我们还提供了 [MMDetection 中文解读文案汇总](docs/zh_cn/article.md)
+
+若需要将2.x版本的代码迁移至新版，请参考[迁移文档](https://mmdetection.readthedocs.io/en/latest/migration.html)。
+
+## 基准测试和模型库
+
+测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
+
+<div align="center">
+  <b>算法架构</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Object Detection</b>
+      </td>
+      <td>
+        <b>Instance Segmentation</b>
+      </td>
+      <td>
+        <b>Panoptic Segmentation</b>
+      </td>
+      <td>
+        <b>Other</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+        <ul>
+            <li><a href="configs/fast_rcnn">Fast R-CNN (ICCV'2015)</a></li>
+            <li><a href="configs/faster_rcnn">Faster R-CNN (NeurIPS'2015)</a></li>
+            <li><a href="configs/rpn">RPN (NeurIPS'2015)</a></li>
+            <li><a href="configs/ssd">SSD (ECCV'2016)</a></li>
+            <li><a href="configs/retinanet">RetinaNet (ICCV'2017)</a></li>
+            <li><a href="configs/cascade_rcnn">Cascade R-CNN (CVPR'2018)</a></li>
+            <li><a href="configs/yolo">YOLOv3 (ArXiv'2018)</a></li>
+            <li><a href="configs/cornernet">CornerNet (ECCV'2018)</a></li>
+            <li><a href="configs/grid_rcnn">Grid R-CNN (CVPR'2019)</a></li>
+            <li><a href="configs/guided_anchoring">Guided Anchoring (CVPR'2019)</a></li>
+            <li><a href="configs/fsaf">FSAF (CVPR'2019)</a></li>
+            <li><a href="configs/centernet">CenterNet (CVPR'2019)</a></li>
+            <li><a href="configs/libra_rcnn">Libra R-CNN (CVPR'2019)</a></li>
+            <li><a href="configs/tridentnet">TridentNet (ICCV'2019)</a></li>
+            <li><a href="configs/fcos">FCOS (ICCV'2019)</a></li>
+            <li><a href="configs/reppoints">RepPoints (ICCV'2019)</a></li>
+            <li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
+            <li><a href="configs/cascade_rpn">CascadeRPN (NeurIPS'2019)</a></li>
+            <li><a href="configs/foveabox">Foveabox (TIP'2020)</a></li>
+            <li><a href="configs/double_heads">Double-Head R-CNN (CVPR'2020)</a></li>
+            <li><a href="configs/atss">ATSS (CVPR'2020)</a></li>
+            <li><a href="configs/nas_fcos">NAS-FCOS (CVPR'2020)</a></li>
+            <li><a href="configs/centripetalnet">CentripetalNet (CVPR'2020)</a></li>
+            <li><a href="configs/autoassign">AutoAssign (ArXiv'2020)</a></li>
+            <li><a href="configs/sabl">Side-Aware Boundary Localization (ECCV'2020)</a></li>
+            <li><a href="configs/dynamic_rcnn">Dynamic R-CNN (ECCV'2020)</a></li>
+            <li><a href="configs/detr">DETR (ECCV'2020)</a></li>
+            <li><a href="configs/paa">PAA (ECCV'2020)</a></li>
+            <li><a href="configs/vfnet">VarifocalNet (CVPR'2021)</a></li>
+            <li><a href="configs/sparse_rcnn">Sparse R-CNN (CVPR'2021)</a></li>
+            <li><a href="configs/yolof">YOLOF (CVPR'2021)</a></li>
+            <li><a href="configs/yolox">YOLOX (CVPR'2021)</a></li>
+            <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
+            <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
+            <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
+            <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+            <li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
+            <li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
+            <li><a href="configs/dino">DINO (ICLR'2023)</a></li>
+            <li><a href="configs/glip">GLIP (CVPR'2022)</a></li>
+            <li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
+            <li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
+            <li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li>
+          <li><a href="configs/cascade_rcnn">Cascade Mask R-CNN (CVPR'2018)</a></li>
+          <li><a href="configs/ms_rcnn">Mask Scoring R-CNN (CVPR'2019)</a></li>
+          <li><a href="configs/htc">Hybrid Task Cascade (CVPR'2019)</a></li>
+          <li><a href="configs/yolact">YOLACT (ICCV'2019)</a></li>
+          <li><a href="configs/instaboost">InstaBoost (ICCV'2019)</a></li>
+          <li><a href="configs/solo">SOLO (ECCV'2020)</a></li>
+          <li><a href="configs/point_rend">PointRend (CVPR'2020)</a></li>
+          <li><a href="configs/detectors">DetectoRS (ArXiv'2020)</a></li>
+          <li><a href="configs/solov2">SOLOv2 (NeurIPS'2020)</a></li>
+          <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
+          <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
+          <li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
+          <li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
+          <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+          <li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
+          <li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        </ul>
+          <li><b>Contrastive Learning</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/selfsup_pretrain">SwAV (NeurIPS'2020)</a></li>
+          <li><a href="configs/selfsup_pretrain">MoCo (CVPR'2020)</a></li>
+          <li><a href="configs/selfsup_pretrain">MoCov2 (ArXiv'2020)</a></li>
+        </ul>
+        </ul>
+        </ul>
+          <li><b>Distillation</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/ld">Localization Distillation (CVPR'2022)</a></li>
+          <li><a href="configs/lad">Label Assignment Distillation (WACV'2022)</a></li>
+        </ul>
+        </ul>
+          <li><b>Semi-Supervised Object Detection</b></li>
+        <ul>
+        <ul>
+          <li><a href="configs/soft_teacher">Soft Teacher (ICCV'2021)</a></li>
+        </ul>
+        </ul>
+      </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+<div align="center">
+  <b>模块组件</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>Backbones</b>
+      </td>
+      <td>
+        <b>Necks</b>
+      </td>
+      <td>
+        <b>Loss</b>
+      </td>
+      <td>
+        <b>Common</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li>VGG (ICLR'2015)</li>
+        <li>ResNet (CVPR'2016)</li>
+        <li>ResNeXt (CVPR'2017)</li>
+        <li>MobileNetV2 (CVPR'2018)</li>
+        <li><a href="configs/hrnet">HRNet (CVPR'2019)</a></li>
+        <li><a href="configs/empirical_attention">Generalized Attention (ICCV'2019)</a></li>
+        <li><a href="configs/gcnet">GCNet (ICCVW'2019)</a></li>
+        <li><a href="configs/res2net">Res2Net (TPAMI'2020)</a></li>
+        <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
+        <li><a href="configs/resnest">ResNeSt (ArXiv'2020)</a></li>
+        <li><a href="configs/pvt">PVT (ICCV'2021)</a></li>
+        <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
+        <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
+        <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
+        <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
+        <li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
+        <li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="configs/pafpn">PAFPN (CVPR'2018)</a></li>
+        <li><a href="configs/nas_fpn">NAS-FPN (CVPR'2019)</a></li>
+        <li><a href="configs/carafe">CARAFE (ICCV'2019)</a></li>
+        <li><a href="configs/fpg">FPG (ArXiv'2020)</a></li>
+        <li><a href="configs/groie">GRoIE (ICPR'2020)</a></li>
+        <li><a href="configs/dyhead">DyHead (CVPR'2021)</a></li>
+      </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/ghm">GHM (AAAI'2019)</a></li>
+          <li><a href="configs/gfl">Generalized Focal Loss (NeurIPS'2020)</a></li>
+          <li><a href="configs/seesaw_loss">Seasaw Loss (CVPR'2021)</a></li>
+        </ul>
+      </td>
+      <td>
+        <ul>
+          <li><a href="configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py">OHEM (CVPR'2016)</a></li>
+          <li><a href="configs/gn">Group Normalization (ECCV'2018)</a></li>
+          <li><a href="configs/dcn">DCN (ICCV'2017)</a></li>
+          <li><a href="configs/dcnv2">DCNv2 (CVPR'2019)</a></li>
+          <li><a href="configs/gn+ws">Weight Standardization (ArXiv'2019)</a></li>
+          <li><a href="configs/pisa">Prime Sample Attention (CVPR'2020)</a></li>
+          <li><a href="configs/strong_baselines">Strong Baselines (CVPR'2021)</a></li>
+          <li><a href="configs/resnet_strikes_back">Resnet strikes back (ArXiv'2021)</a></li>
+        </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+我们在[基于 MMDetection 的项目](./docs/zh_cn/notes/projects.md)中列举了一些其他的支持的算法。
+
+## 常见问题
+
+请参考 [FAQ](docs/zh_cn/notes/faq.md) 了解其他用户的常见问题。
+
+## 贡献指南
+
+我们感谢所有的贡献者为改进和提升 MMDetection 所作出的努力。我们将正在进行中的项目添加进了[GitHub Projects](https://github.com/open-mmlab/mmdetection/projects)页面，非常欢迎社区用户能参与进这些项目中来。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
+
+## 致谢
+
+MMDetection 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。
+
+## 引用
+
+如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMDetection。
+
+```
+@article{mmdetection,
+  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
+  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
+             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
+             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
+             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
+             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
+             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
+  journal= {arXiv preprint arXiv:1906.07155},
+  year={2019}
+}
+```
+
+## 开源许可证
+
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+
+## OpenMMLab 的其他项目
+
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
+- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
+- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
+- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
+- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
+- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
+- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
+- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
+- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+- [MIM](https://github.com/open-mmlab/mim): OpenMMlab 项目、算法、模型的统一入口
+- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
+- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
+
+## 欢迎加入 OpenMMLab 社区
+
+扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3)
+
+<div align="center">
+<img src="resources/zhihu_qrcode.jpg" height="400" />  <img src="resources/qq_group_qrcode.jpg" height="400" />
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

mmdetection/configs/_base_/datasets/ade20k_instance.py

@@ -0,0 +1,53 @@
+# dataset settings
+dataset_type = 'ADE20KInstanceDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2560, 640), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='ade20k_instance_val.json',
+        data_prefix=dict(img='images/validation'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'ade20k_instance_val.json',
+    metric=['bbox', 'segm'],
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/ade20k_panoptic.py

@@ -0,0 +1,38 @@
+# dataset settings
+dataset_type = 'ADE20KPanopticDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2560, 640), keep_ratio=True),
+    dict(type='LoadPanopticAnnotations', backend_args=backend_args),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=0,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='ade20k_panoptic_val.json',
+        data_prefix=dict(img='images/validation/', seg='ade20k_panoptic_val/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoPanopticMetric',
+    ann_file=data_root + 'ade20k_panoptic_val.json',
+    seg_prefix=data_root + 'ade20k_panoptic_val/',
+    backend_args=backend_args)
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/ade20k_semantic.py

@@ -0,0 +1,48 @@
+dataset_type = 'ADE20KSegDataset'
+data_root = 'data/ADEChallengeData2016/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/ADEChallengeData2016/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 512), keep_ratio=True),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=False,
+        with_mask=False,
+        with_seg=True,
+        reduce_zero_label=True),
+    dict(
+        type='PackDetInputs', meta_keys=('img_path', 'ori_shape', 'img_shape'))
+]
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='images/validation',
+            seg_map_path='annotations/validation'),
+        pipeline=test_pipeline))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/cityscapes_detection.py

@@ -0,0 +1,84 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='RandomResize',
+        scale=[(2048, 800), (2048, 1024)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='annotations/instancesonly_filtered_gtFine_train.json',
+            data_prefix=dict(img='leftImg8bit/train/'),
+            filter_cfg=dict(filter_empty_gt=True, min_size=32),
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instancesonly_filtered_gtFine_val.json',
+        data_prefix=dict(img='leftImg8bit/val/'),
+        test_mode=True,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/instancesonly_filtered_gtFine_val.json',
+    metric='bbox',
+    backend_args=backend_args)
+
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/cityscapes_instance.py

@@ -0,0 +1,113 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/segmentation/cityscapes/'
+
+# Method 2: Use backend_args, file_client_args in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/segmentation/',
+#          'data/': 's3://openmmlab/datasets/segmentation/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='RandomResize',
+        scale=[(2048, 800), (2048, 1024)],
+        keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(2048, 1024), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='annotations/instancesonly_filtered_gtFine_train.json',
+            data_prefix=dict(img='leftImg8bit/train/'),
+            filter_cfg=dict(filter_empty_gt=True, min_size=32),
+            pipeline=train_pipeline,
+            backend_args=backend_args)))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instancesonly_filtered_gtFine_val.json',
+        data_prefix=dict(img='leftImg8bit/val/'),
+        test_mode=True,
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = [
+    dict(
+        type='CocoMetric',
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_val.json',
+        metric=['bbox', 'segm'],
+        backend_args=backend_args),
+    dict(
+        type='CityScapesMetric',
+        seg_prefix=data_root + 'gtFine/val',
+        outfile_prefix='./work_dirs/cityscapes_metric/instance',
+        backend_args=backend_args)
+]
+
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=2,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file='annotations/instancesonly_filtered_gtFine_test.json',
+#         data_prefix=dict(img='leftImg8bit/test/'),
+#         test_mode=True,
+#         filter_cfg=dict(filter_empty_gt=True, min_size=32),
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#         type='CityScapesMetric',
+#         format_only=True,
+#         outfile_prefix='./work_dirs/cityscapes_metric/test')

mmdetection/configs/_base_/datasets/coco_caption.py

@@ -0,0 +1,60 @@
+# data settings
+
+dataset_type = 'CocoCaptionDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+test_pipeline = [
+    dict(
+        type='LoadImageFromFile',
+        imdecode_backend='pillow',
+        backend_args=backend_args),
+    dict(
+        type='Resize',
+        scale=(224, 224),
+        interpolation='bicubic',
+        backend='pillow'),
+    dict(type='PackInputs', meta_keys=['image_id']),
+]
+
+# ann_file download from
+# train dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json # noqa
+# val dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json # noqa
+# test dataset: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json # noqa
+# val evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json # noqa
+# test evaluator: https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json # noqa
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/coco_karpathy_val.json',
+        pipeline=test_pipeline,
+    ))
+
+val_evaluator = dict(
+    type='COCOCaptionMetric',
+    ann_file=data_root + 'annotations/coco_karpathy_val_gt.json',
+)
+
+# # If you want standard test, please manually configure the test dataset
+test_dataloader = val_dataloader
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/coco_detection.py

@@ -0,0 +1,95 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_train2017.json',
+        data_prefix=dict(img='train2017/'),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric='bbox',
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=2,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#         data_prefix=dict(img='test2017/'),
+#         test_mode=True,
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#     type='CocoMetric',
+#     metric='bbox',
+#     format_only=True,
+#     ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#     outfile_prefix='./work_dirs/coco_detection/test')

mmdetection/configs/_base_/datasets/coco_instance.py

@@ -0,0 +1,98 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/'
+
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/annotations/instances_train2014.json',
+        data_prefix=dict(img='/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/images/train2014/'),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=4,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        # ann_file='/mnt/bn/panxuran/instances_val2014_part_1000.json',
+        ann_file='/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/annotations/instances_val2014.json',
+        data_prefix=dict(img='/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/images/val2014/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    # ann_file='/mnt/bn/panxuran/instances_val2014_part_1000.json',
+    ann_file='/mnt/bn/zhicheng-dev-v6/dataset/coco-caption/annotations/instances_val2014.json',
+    metric=['segm'],
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=2,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#         data_prefix=dict(img='test2017/'),
+#         test_mode=True,
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#     type='CocoMetric',
+#     metric=['bbox', 'segm'],
+#     format_only=True,
+#     ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#     outfile_prefix='./work_dirs/coco_instance/test')

mmdetection/configs/_base_/datasets/coco_instance_semantic.py

@@ -0,0 +1,78 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_train2017.json',
+        data_prefix=dict(img='train2017/', seg='stuffthingmaps/train2017/'),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/instances_val2017.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/instances_val2017.json',
+    metric=['bbox', 'segm'],
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/coco_panoptic.py

@@ -0,0 +1,94 @@
+# dataset settings
+dataset_type = 'CocoPanopticDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadPanopticAnnotations', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='LoadPanopticAnnotations', backend_args=backend_args),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+
+train_dataloader = dict(
+    batch_size=1,
+    num_workers=0,
+    persistent_workers=False,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/panoptic_train2017.json',
+        data_prefix=dict(
+            img='train2017/', seg='annotations/panoptic_train2017/'),
+        filter_cfg=dict(filter_empty_gt=True, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/panoptic_val2017.json',
+        data_prefix=dict(img='val2017/', seg='annotations/panoptic_val2017/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoPanopticMetric',
+    ann_file=data_root + 'annotations/panoptic_val2017.json',
+    seg_prefix=data_root + 'annotations/panoptic_val2017/',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=1,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file='annotations/panoptic_image_info_test-dev2017.json',
+#         data_prefix=dict(img='test2017/'),
+#         test_mode=True,
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#     type='CocoPanopticMetric',
+#     format_only=True,
+#     ann_file=data_root + 'annotations/panoptic_image_info_test-dev2017.json',
+#     outfile_prefix='./work_dirs/coco_panoptic/test')

mmdetection/configs/_base_/datasets/coco_semantic.py

@@ -0,0 +1,78 @@
+# dataset settings
+dataset_type = 'CocoSegDataset'
+data_root = 'data/coco/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=False,
+        with_label=False,
+        with_seg=True),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(1333, 800), keep_ratio=True),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=False,
+        with_label=False,
+        with_seg=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
+]
+
+# For stuffthingmaps_semseg, please refer to
+# `docs/en/user_guides/dataset_prepare.md`
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='train2017/',
+            seg_map_path='stuffthingmaps_semseg/train2017/'),
+        pipeline=train_pipeline))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            img_path='val2017/',
+            seg_map_path='stuffthingmaps_semseg/val2017/'),
+        pipeline=test_pipeline))
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(type='SemSegMetric', iou_metrics=['mIoU'])
+test_evaluator = val_evaluator

mmdetection/configs/_base_/datasets/ct_detection.py

@@ -0,0 +1,96 @@
+
+# dataset settings
+dataset_type = 'CocoCTDataset'
+data_root = '/mnt/bn/panxuran/Slice_Data/slice_dataset_maximum_0402/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection/coco/'
+
+# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', scale=(512, 512), keep_ratio=True),
+    dict(type='RandomFlip', prob=0.5),
+    dict(type='PackDetInputs')
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='Resize', scale=(512, 512), keep_ratio=True),
+    # If you don't have a gt annotation, delete the pipeline
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=dict(type='AspectRatioBatchSampler'),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/train_wsyn.json',
+        data_prefix=dict(img='images/train/'),
+        filter_cfg=dict(filter_empty_gt=False, min_size=32),
+        pipeline=train_pipeline,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='annotations/test.json',
+        data_prefix=dict(img='images/test/'),
+        test_mode=True,
+        pipeline=test_pipeline,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='CocoMetric',
+    ann_file=data_root + 'annotations/test.json',
+    metric='bbox',
+    format_only=False,
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+# inference on test dataset and
+# format the output results for submission.
+# test_dataloader = dict(
+#     batch_size=1,
+#     num_workers=2,
+#     persistent_workers=True,
+#     drop_last=False,
+#     sampler=dict(type='DefaultSampler', shuffle=False),
+#     dataset=dict(
+#         type=dataset_type,
+#         data_root=data_root,
+#         ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#         data_prefix=dict(img='test2017/'),
+#         test_mode=True,
+#         pipeline=test_pipeline))
+# test_evaluator = dict(
+#     type='CocoMetric',
+#     metric='bbox',
+#     format_only=True,
+#     ann_file=data_root + 'annotations/image_info_test-dev2017.json',
+#     outfile_prefix='./work_dirs/coco_detection/test')